library(dplyr)
library(lavaan)
library(DiagrammeR)
library(ggplot2)
library(tidyr)
source("functions/table_funcs.R")
# For saving SEM diagrams:
library(purrr)
library(DiagrammeRsvg)
library(rsvg)
library(png)
library(grid)
library(ggpubr)

Import data

combined=read.csv("data/monthly_averages/monthly_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region_monthly.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1995) %>% arrange(Region,Year,Month)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("Far West","West","North","South")
focaldata=focaldata%>% 
  mutate(decyear=year+(month-1)/12)
focaldata = focaldata %>% 
  mutate(tzoop=hcope+clad+mysid+pcope+rotif_m,
         tzoop_e=hcope_e+clad_e+mysid_e+pcope_e+rotif_e,
         hzoop=hcope+clad+rotif_m,
         hzoop_e=hcope_e+clad_e+rotif_e,
         pzoop=mysid+pcope,
         pzoop_e=mysid_e+pcope_e,
         turbid=-secchi) 
fvars=c(fvars,"tzoop","tzoop_e",
        "hzoop","hzoop_e",
        "pzoop","pzoop_e","turbid")
cnames=rbind(cnames,data.frame(Longname=NA,Shortname=c("tzoop","tzoop_e",
                                                       "hzoop","hzoop_e",
                                                       "pzoop","pzoop_e","turbid"),
                               Diagramname=c("total zooplankton",
                                             "total zooplankton\nenergy",
                                             "herbivorous\nzooplankton",
                                             "herbivorous\nzooplankton\nenergy",
                                             "predatory\nzooplankton",
                                             "predatory\nzooplankton\nenergy",
                                             "turbidity"),
                               Datacolumn=NA,Log=c(rep("yes",6),"no"),
                               Color=c("black","black","#ED7D31","#ED7D31","#7030A0",
                                       "#7030A0","#4472C4")))
#focal variables
varnames=c("temp","flow","turbid","din","chla","hzoop","pzoop","potam","corbic","estfish_bsmt","sside","cent","sbass1_bsmt","marfish_bsmt","hcope","clad","amphi_m","pcope","mysid","rotif_m")
#labels for lagged vars
cnameslag=cnames
cnameslag$Shortname=paste0(cnameslag$Shortname,"_1")
cnameslag$Diagramname=paste(cnameslag$Diagramname,"(t-1)")
cnameslag=rbind(cnames,cnameslag)
#labeld for growth rate
cnamesgr=cnames
cnamesgr$Shortname=paste0(cnamesgr$Shortname,"_gr")
cnamesgr$Diagramname=paste(cnamesgr$Diagramname,"(gr)")
cnameslag=rbind(cnameslag,cnamesgr)
source("analysis/myLavaanPlot.r")
source("analysis/semDiagramFunctions.r")

Data prep

Log transform, scale.
Within and across regions.
Create set with regional monthly means removed.

#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
  x2=x[which(!is.na(x))]
  if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
  else {log(x)}
}
focaldatalog = focaldata %>% 
  mutate(flow=flow-min(flow,na.rm=T)) %>%  #get rid of negative flow values
  mutate_at(logvars,logtrans) %>% 
  group_by(region) %>%
  mutate_at(logvars,list("gr"=function(x) {c(NA,diff(x))})) %>% 
  ungroup()
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:3)]
#scaled within regions
fdr=fdr0 %>% 
  group_by(region) %>% 
  #scale
  mutate_at(tvars,scale) %>% 
  #lag
  mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>% 
  ungroup() %>% 
  as.data.frame()
#scaled within regions, remove monthly means
fdr_ds=fdr %>% 
  group_by(region,month) %>%
  mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>% 
  mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>% 
  ungroup() %>% 
  #lag
  group_by(region) %>% 
  mutate_at(tvars,scale) %>% 
  mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>% 
  ungroup() %>% 
  as.data.frame()
#scaled across regions
# fdr1=fdr0 %>% 
#   #scale
#   mutate_at(tvars,scale) %>% 
#   #lag
#   group_by(region) %>% 
#   mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>% 
#   ungroup() %>% 
#   as.data.frame()
#scaled across regions, monthly means removed
# fdr1_ds=fdr1 %>% 
#   group_by(region,month) %>%
#   mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>% 
#   mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>% 
#   ungroup() %>% 
#   #lag
#   group_by(region) %>% 
#   mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>% 
#   ungroup() %>% 
#   as.data.frame()

Data availability

Exclude individual zooplankton plankton groups from zooplankton model if rare (95% of values in a region are less than the across site mean, or more than 10% of values in a region are zeros).

sside and cent have no data in FW and W.

marfish and clams excluded if 95% of values in a region are less than the across site mean, though this results in marfish being excluded from W.

FW: exclude clad, mysid, corbic, sside/cent
W: exclude clad, corbic, marfish, sside/cent
N: exclude clad, potam, marfish
S: exclude mysid, potam, marfish

dataavail=focaldata %>% 
  gather(var, value, 4:length(fvars)) %>% 
  group_by(var) %>% 
  mutate(varmean=mean(value, na.rm=T)) %>% ungroup() %>% 
  group_by(region, var) %>% 
  summarize(
    propmissing=length(which(is.na(value)))/length(value),
    propzeros=length(which(value==0))/length(which(!is.na(value))),
    exclude=ifelse(quantile(value,probs = 0.95, na.rm = T)<mean(varmean),T,F)) %>% 
  as.data.frame()
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
#these variables should not be used (too many zeros)
filter(dataavail,propzeros>0.1 | exclude) %>% filter(var %in% c("mysid","hcope","pcope","rotif_m","clad"))
##     region   var propmissing  propzeros exclude
## 1 Far West  clad 0.141025641 0.72388060    TRUE
## 2 Far West mysid 0.137820513 0.21933086    TRUE
## 3    North  clad 0.012820513 0.11363636   FALSE
## 4    South mysid 0.006410256 0.08709677    TRUE
## 5     West  clad 0.009615385 0.20064725   FALSE
filter(dataavail,exclude) %>% filter(var %in% c("marfish_bsmt","potam","corbic"))
##     region          var propmissing propzeros exclude
## 1 Far West       corbic   0.1410256 1.0000000    TRUE
## 2    North marfish_bsmt   0.1891026 0.9762846    TRUE
## 3    North        potam   0.1378205 0.1486989    TRUE
## 4    South marfish_bsmt   0.1826923 1.0000000    TRUE
## 5    South        potam   0.1378205 0.9702602    TRUE
## 6     West       corbic   0.1378205 0.8066914    TRUE
## 7     West marfish_bsmt   0.1666667 0.2192308    TRUE

Time series plots

## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(varnames)` instead of `varnames` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

Other useful plots

Breakdown of total zooplankton biomass.

## Warning: Removed 272 rows containing missing values (position_stack).

Correlation between biomass and energy.

for(i in 1:length(regions)) {
  dtemp=filter(fdr,region==regions[i])
  print(regions[i])
  print(cor(dtemp$tzoop,dtemp$tzoop_e,use = "p"))
  print(cor(dtemp$hzoop,dtemp$hzoop_e,use = "p"))
  print(cor(dtemp$pzoop,dtemp$pzoop_e,use = "p"))
}
## [1] "Far West"
##           [,1]
## [1,] 0.9941835
##           [,1]
## [1,] 0.9945149
##           [,1]
## [1,] 0.9996106
## [1] "North"
##           [,1]
## [1,] 0.9938912
##           [,1]
## [1,] 0.9903659
##          [,1]
## [1,] 0.999494
## [1] "South"
##           [,1]
## [1,] 0.9955739
##           [,1]
## [1,] 0.9952125
##         [,1]
## [1,] 0.99925
## [1] "West"
##           [,1]
## [1,] 0.9797348
##           [,1]
## [1,] 0.9374501
##           [,1]
## [1,] 0.9983814

Cross-correlation matrices

(only sig correlations shown… no correction for multiple comparisons)

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

## Warning: `expand_scale()` is deprecated; use `expansion()` instead.

Other notes:

Detrended fish indices are NOT correlated in S!

Nitrate and ammonia are positively correlated, max at lag 0 all regions.
Nitrate and dophos are positively correlated, max at lag 0 all regions.
Ammonia and dophos are positively correlated, lag 0 for FW and S, ammonia lags dphos by 3 months in W and N.

Chla nitrate neg correlated, lag 0.
Chla ammonia neg correlated, lag 0.
Chla dophos relationship unclear.

High flow 2-4 month prev = high chla

Hcope lags chla by 1, positive, except FW.
Clad seem to precede chla by 2, positive.
Amphi relationship unclear, prob bc not eating chla in water column.
In N and W, chla lags potam, negative. The opposite in W.

Mysid and hcope postive, lag 0.
In S and W, hcope lags pcope, negative.

Exploratory plots

Fish-centered model (upper trophic level aggregates)

modFW='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop~pb1*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*marfish_bsmt_1+ft2*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modW='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modN='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2+ft3^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modS='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt~fb1*chla_1+fb2*hzoop_1+fb3*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2+fb3^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2+ft3^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 14 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        29
##                                                       
##                                                   Used       Total
##   Number of observations                           191         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 7.294
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.399
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop ~                                                               
##     chla_1   (hb1)    0.019    0.076    0.246    0.806    0.019    0.016
##     hzoop_1  (hs1)    0.313    0.065    4.820    0.000    0.313    0.324
##     pzoop_1  (ht1)    0.062    0.062    0.997    0.319    0.062    0.066
##     potam_1  (ht2)   -0.190    0.058   -3.263    0.001   -0.190   -0.213
##     estfs__1 (ht3)   -0.175    0.069   -2.536    0.011   -0.175   -0.174
##     flow     (ha1)   -0.022    0.075   -0.298    0.766   -0.022   -0.022
##     temp     (ha2)   -0.080    0.072   -1.104    0.270   -0.080   -0.076
##     turbid   (ha3)    0.042    0.080    0.522    0.602    0.042    0.038
##   pzoop ~                                                               
##     hzoop_1  (pb1)    0.054    0.067    0.810    0.418    0.054    0.054
##     pzoop_1  (ps1)    0.344    0.065    5.278    0.000    0.344    0.348
##     potam_1  (pt1)   -0.102    0.060   -1.697    0.090   -0.102   -0.110
##     estfs__1 (pt2)   -0.031    0.072   -0.428    0.669   -0.031   -0.029
##     flow     (pa1)    0.082    0.078    1.050    0.294    0.082    0.076
##     temp     (pa2)   -0.020    0.075   -0.268    0.789   -0.020   -0.018
##     turbid   (pa3)    0.252    0.084    3.006    0.003    0.252    0.215
##   estfish_bsmt ~                                                        
##     hzoop_1  (fb1)   -0.178    0.056   -3.151    0.002   -0.178   -0.192
##     pzoop_1  (fb2)    0.116    0.056    2.076    0.038    0.116    0.128
##     estfs__1 (fs1)    0.343    0.064    5.369    0.000    0.343    0.355
##     flow     (fa1)    0.092    0.068    1.349    0.177    0.092    0.092
##     temp     (fa2)   -0.032    0.065   -0.496    0.620   -0.032   -0.032
##     turbid   (fa3)    0.246    0.073    3.389    0.001    0.246    0.230
##     mrfsh__1 (ft1)    0.001    0.064    0.018    0.986    0.001    0.001
##     sbss1__1 (ft2)   -0.018    0.065   -0.281    0.778   -0.018   -0.018
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop ~~                                                              
##    .pzoop            -0.035    0.058   -0.595    0.552   -0.035   -0.043
##    .estfish_bsmt     -0.046    0.050   -0.918    0.359   -0.046   -0.067
##  .pzoop ~~                                                              
##    .estfish_bsmt     -0.088    0.052   -1.684    0.092   -0.088   -0.123
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop             0.772    0.079    9.772    0.000    0.772    0.754
##    .pzoop             0.842    0.086    9.772    0.000    0.842    0.750
##    .estfish_bsmt      0.606    0.062    9.772    0.000    0.606    0.641
## 
## R-Square:
##                    Estimate
##     hzoop             0.246
##     pzoop             0.250
##     estfish_bsmt      0.359
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.019    0.076    0.246    0.806    0.019    0.016
##     hs                0.313    0.065    4.820    0.000    0.313    0.324
##     ht                0.266    0.057    4.623    0.000    0.266    0.283
##     ha                0.093    0.072    1.288    0.198    0.093    0.087
##     pb                0.054    0.067    0.810    0.418    0.054    0.054
##     ps                0.344    0.065    5.278    0.000    0.344    0.348
##     pt                0.107    0.058    1.837    0.066    0.107    0.113
##     pa                0.265    0.073    3.627    0.000    0.265    0.229
##     fb                0.212    0.059    3.618    0.000    0.212    0.231
##     fs                0.343    0.064    5.369    0.000    0.343    0.355
##     ft                0.018    0.064    0.283    0.777    0.018    0.018
##     fa                0.265    0.062    4.262    0.000    0.265    0.249
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 17 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        29
##                                                       
##                                                   Used       Total
##   Number of observations                           210         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 2.534
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.638
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop ~                                                               
##     chla_1   (hb1)    0.089    0.068    1.313    0.189    0.089    0.080
##     hzoop_1  (hs1)    0.359    0.071    5.031    0.000    0.359    0.348
##     pzoop_1  (ht1)    0.034    0.064    0.528    0.597    0.034    0.033
##     potam_1  (ht2)   -0.231    0.069   -3.373    0.001   -0.231   -0.216
##     estfs__1 (ht3)   -0.017    0.063   -0.268    0.789   -0.017   -0.016
##     flow     (ha1)    0.197    0.070    2.831    0.005    0.197    0.184
##     temp     (ha2)    0.026    0.061    0.433    0.665    0.026    0.026
##     turbid   (ha3)   -0.203    0.068   -2.982    0.003   -0.203   -0.188
##   pzoop ~                                                               
##     chla_1   (pb1)    0.192    0.065    2.964    0.003    0.192    0.179
##     hzoop_1  (pb2)    0.117    0.069    1.698    0.089    0.117    0.117
##     pzoop_1  (ps1)    0.415    0.062    6.679    0.000    0.415    0.411
##     potam_1  (pt1)   -0.086    0.066   -1.301    0.193   -0.086   -0.082
##     estfs__1 (pt2)    0.037    0.061    0.603    0.546    0.037    0.036
##     flow     (pa1)   -0.105    0.068   -1.556    0.120   -0.105   -0.101
##     temp     (pa2)    0.186    0.059    3.160    0.002    0.186    0.189
##     turbid   (pa3)    0.035    0.066    0.530    0.596    0.035    0.033
##   estfish_bsmt ~                                                        
##     hzoop_1  (fb1)    0.135    0.071    1.896    0.058    0.135    0.132
##     pzoop_1  (fb2)   -0.079    0.069   -1.147    0.252   -0.079   -0.077
##     estfs__1 (fs1)    0.175    0.072    2.427    0.015    0.175    0.169
##     flow     (fa1)   -0.221    0.074   -2.984    0.003   -0.221   -0.210
##     temp     (fa2)    0.017    0.064    0.265    0.791    0.017    0.017
##     turbid   (fa3)    0.248    0.071    3.502    0.000    0.248    0.232
##     sbss1__1 (ft1)    0.234    0.069    3.392    0.001    0.234    0.225
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop ~~                                                              
##    .pzoop             0.178    0.049    3.639    0.000    0.178    0.259
##    .estfish_bsmt     -0.077    0.053   -1.474    0.141   -0.077   -0.102
##  .pzoop ~~                                                              
##    .estfish_bsmt      0.137    0.052    2.660    0.008    0.137    0.187
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop             0.707    0.069   10.247    0.000    0.707    0.668
##    .pzoop             0.667    0.065   10.247    0.000    0.667    0.668
##    .estfish_bsmt      0.813    0.079   10.247    0.000    0.813    0.787
## 
## R-Square:
##                    Estimate
##     hzoop             0.332
##     pzoop             0.332
##     estfish_bsmt      0.213
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.089    0.068    1.313    0.189    0.089    0.080
##     hs                0.359    0.071    5.031    0.000    0.359    0.348
##     ht                0.234    0.069    3.405    0.001    0.234    0.219
##     ha                0.284    0.076    3.756    0.000    0.284    0.264
##     pb                0.225    0.057    3.952    0.000    0.225    0.214
##     ps                0.415    0.062    6.679    0.000    0.415    0.411
##     pt                0.093    0.062    1.493    0.136    0.093    0.090
##     pa                0.216    0.063    3.456    0.001    0.216    0.217
##     fb                0.156    0.080    1.956    0.051    0.156    0.153
##     fs                0.175    0.072    2.427    0.015    0.175    0.169
##     ft                0.234    0.069    3.392    0.001    0.234    0.225
##     fa                0.333    0.082    4.057    0.000    0.333    0.313
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        31
##                                                       
##                                                   Used       Total
##   Number of observations                           193         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 8.135
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.420
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop ~                                                               
##     chla_1   (hb1)    0.036    0.072    0.501    0.617    0.036    0.033
##     hzoop_1  (hs1)    0.216    0.070    3.072    0.002    0.216    0.212
##     pzoop_1  (ht1)    0.040    0.073    0.549    0.583    0.040    0.039
##     corbic_1 (ht2)    0.004    0.068    0.065    0.948    0.004    0.004
##     estfs__1 (ht3)   -0.079    0.068   -1.158    0.247   -0.079   -0.084
##     flow     (ha1)    0.242    0.075    3.208    0.001    0.242    0.242
##     temp     (ha2)    0.107    0.066    1.625    0.104    0.107    0.111
##     turbid   (ha3)    0.190    0.070    2.726    0.006    0.190    0.185
##   pzoop ~                                                               
##     chla_1   (pb1)    0.266    0.064    4.135    0.000    0.266    0.245
##     hzoop_1  (pb2)    0.177    0.063    2.822    0.005    0.177    0.173
##     pzoop_1  (ps1)    0.233    0.065    3.590    0.000    0.233    0.229
##     corbic_1 (pt1)    0.030    0.060    0.503    0.615    0.030    0.030
##     estfs__1 (pt2)   -0.059    0.060   -0.977    0.329   -0.059   -0.063
##     flow     (pa1)   -0.435    0.067   -6.487    0.000   -0.435   -0.434
##     temp     (pa2)    0.080    0.059    1.366    0.172    0.080    0.083
##     turbid   (pa3)    0.004    0.062    0.067    0.946    0.004    0.004
##   estfish_bsmt ~                                                        
##     hzoop_1  (fb1)    0.152    0.074    2.057    0.040    0.152    0.139
##     pzoop_1  (fb2)    0.012    0.075    0.162    0.871    0.012    0.011
##     estfs__1 (fs1)    0.130    0.071    1.824    0.068    0.130    0.129
##     flow     (fa1)   -0.468    0.078   -6.001    0.000   -0.468   -0.435
##     temp     (fa2)   -0.016    0.067   -0.237    0.813   -0.016   -0.015
##     turbid   (fa3)    0.066    0.073    0.911    0.362    0.066    0.060
##     sside_1  (ft1)   -0.018    0.068   -0.262    0.793   -0.018   -0.017
##     cent_1   (ft2)   -0.132    0.069   -1.914    0.056   -0.132   -0.125
##     sbss1__1 (ft3)    0.095    0.071    1.331    0.183    0.095    0.090
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop ~~                                                              
##    .pzoop             0.178    0.053    3.359    0.001    0.178    0.249
##    .estfish_bsmt     -0.059    0.059   -0.995    0.320   -0.059   -0.072
##  .pzoop ~~                                                              
##    .estfish_bsmt     -0.001    0.052   -0.028    0.978   -0.001   -0.002
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop             0.803    0.082    9.823    0.000    0.803    0.816
##    .pzoop             0.633    0.064    9.823    0.000    0.633    0.643
##    .estfish_bsmt      0.830    0.085    9.823    0.000    0.830    0.731
## 
## R-Square:
##                    Estimate
##     hzoop             0.184
##     pzoop             0.357
##     estfish_bsmt      0.269
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.036    0.072    0.501    0.617    0.036    0.033
##     hs                0.216    0.070    3.072    0.002    0.216    0.212
##     ht                0.088    0.075    1.171    0.242    0.088    0.093
##     ha                0.326    0.071    4.617    0.000    0.326    0.324
##     pb                0.319    0.062    5.157    0.000    0.319    0.300
##     ps                0.233    0.065    3.590    0.000    0.233    0.229
##     pt                0.066    0.063    1.058    0.290    0.066    0.070
##     pa                0.442    0.065    6.826    0.000    0.442    0.442
##     fb                0.152    0.072    2.107    0.035    0.152    0.139
##     fs                0.130    0.071    1.824    0.068    0.130    0.129
##     ft                0.163    0.063    2.586    0.010    0.163    0.156
##     fa                0.473    0.080    5.935    0.000    0.473    0.440
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 14 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        32
##                                                       
##                                                   Used       Total
##   Number of observations                           199         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 5.739
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.571
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop ~                                                               
##     chla_1   (hb1)    0.254    0.072    3.525    0.000    0.254    0.237
##     hzoop_1  (hs1)    0.215    0.069    3.115    0.002    0.215    0.206
##     pzoop_1  (ht1)   -0.024    0.076   -0.322    0.747   -0.024   -0.022
##     corbic_1 (ht2)    0.073    0.070    1.041    0.298    0.073    0.068
##     estfs__1 (ht3)    0.007    0.069    0.099    0.921    0.007    0.007
##     flow     (ha1)    0.214    0.071    2.995    0.003    0.214    0.196
##     temp     (ha2)    0.172    0.066    2.585    0.010    0.172    0.169
##     turbid   (ha3)   -0.041    0.068   -0.601    0.548   -0.041   -0.040
##   pzoop ~                                                               
##     chla_1   (pb1)    0.295    0.061    4.854    0.000    0.295    0.294
##     hzoop_1  (pb2)    0.149    0.058    2.558    0.011    0.149    0.153
##     pzoop_1  (ps1)    0.324    0.064    5.095    0.000    0.324    0.321
##     corbic_1 (pt1)   -0.062    0.058   -1.066    0.286   -0.062   -0.062
##     estfs__1 (pt2)    0.020    0.058    0.344    0.731    0.020    0.021
##     flow     (pa1)   -0.132    0.060   -2.186    0.029   -0.132   -0.129
##     temp     (pa2)   -0.035    0.056   -0.627    0.530   -0.035   -0.037
##     turbid   (pa3)    0.096    0.057    1.694    0.090    0.096    0.101
##   estfish_bsmt ~                                                        
##     chla_1   (fb1)    0.141    0.070    2.002    0.045    0.141    0.135
##     hzoop_1  (fb2)    0.143    0.068    2.097    0.036    0.143    0.142
##     pzoop_1  (fb3)   -0.049    0.074   -0.671    0.502   -0.049   -0.047
##     estfs__1 (fs1)    0.192    0.068    2.814    0.005    0.192    0.196
##     flow     (fa1)   -0.095    0.071   -1.351    0.177   -0.095   -0.090
##     temp     (fa2)   -0.028    0.065   -0.430    0.667   -0.028   -0.028
##     turbid   (fa3)    0.210    0.071    2.945    0.003    0.210    0.212
##     sside_1  (ft1)    0.056    0.065    0.873    0.383    0.056    0.056
##     cent_1   (ft2)   -0.075    0.067   -1.115    0.265   -0.075   -0.078
##     sbss1__1 (ft3)    0.066    0.068    0.957    0.338    0.066    0.066
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop ~~                                                              
##    .pzoop             0.117    0.051    2.296    0.022    0.117    0.165
##    .estfish_bsmt      0.043    0.058    0.747    0.455    0.043    0.053
##  .pzoop ~~                                                              
##    .estfish_bsmt      0.131    0.049    2.648    0.008    0.131    0.191
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop             0.839    0.084    9.975    0.000    0.839    0.811
##    .pzoop             0.594    0.060    9.975    0.000    0.594    0.661
##    .estfish_bsmt      0.785    0.079    9.975    0.000    0.785    0.811
## 
## R-Square:
##                    Estimate
##     hzoop             0.189
##     pzoop             0.339
##     estfish_bsmt      0.189
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.254    0.072    3.525    0.000    0.254    0.237
##     hs                0.215    0.069    3.115    0.002    0.215    0.206
##     ht                0.077    0.072    1.070    0.285    0.077    0.072
##     ha                0.277    0.071    3.928    0.000    0.277    0.262
##     pb                0.330    0.057    5.768    0.000    0.330    0.332
##     ps                0.324    0.064    5.095    0.000    0.324    0.321
##     pt                0.065    0.057    1.134    0.257    0.065    0.065
##     pa                0.167    0.062    2.700    0.007    0.167    0.168
##     fb                0.207    0.070    2.962    0.003    0.207    0.202
##     fs                0.192    0.068    2.814    0.005    0.192    0.196
##     ft                0.114    0.065    1.762    0.078    0.114    0.117
##     fa                0.233    0.076    3.052    0.002    0.233    0.232
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitS)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Upper trophic level aggregates")
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
upper_plot_far_west <- createGraph(fit=modfitFW, 
                                   reference_df=cnameslag, 
                                   model_type="monthly_upper_trophic",
                                   title="Far West",
                                   manual_port_settings=TRUE)
upper_plot_far_west
#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
upper_plot_west <- createGraph(fit=modfitW, 
                               reference_df=cnameslag, 
                               model_type="monthly_upper_trophic",
                               title="West",
                               manual_port_settings=TRUE)
upper_plot_west
#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
upper_plot_north <- createGraph(fit=modfitN, 
                                reference_df=cnameslag, 
                                model_type="monthly_upper_trophic",
                                title="North",
                                manual_port_settings=TRUE)
upper_plot_north
#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
upper_plot_south <- createGraph(fit=modfitS, 
                                reference_df=cnameslag, 
                                model_type="monthly_upper_trophic",
                                title="South",
                                manual_port_settings=TRUE)
upper_plot_south

Save updated SEM diagrams

Total effects

ssFW=standardizedsolution(modfitFW) %>% mutate(region="Far West")
ssW=standardizedsolution(modfitW) %>% mutate(region="West")
ssN=standardizedsolution(modfitN) %>% mutate(region="North")
ssS=standardizedsolution(modfitS) %>% mutate(region="South")
ssut=rbind(ssFW,ssW,ssN,ssS) %>% filter(op==":=") %>% select(region,lhs,est.std:ci.upper) %>% 
  separate(lhs,c("variable","influence"), sep=1) %>% 
  mutate(variable=case_when(variable=="h" ~ "herbivorous\nzooplankton",
                            variable=="p" ~ "predatory\nzooplankton",
                            variable=="f" ~ "estuarine\nfishes"),
         influence=case_when(influence=="b" ~ "bottom-up",
                            influence=="t" ~ "top-down",
                            influence=="s" ~ "self-regulation",
                            influence=="a" ~ "abiotic drivers"),
         region=factor(region, levels=regionorder),
         influence=factor(influence, levels=c("self-regulation","bottom-up","top-down","abiotic drivers")),
         variable=factor(variable,levels=c("estuarine\nfishes","predatory\nzooplankton","herbivorous\nzooplankton")),
         sig=ifelse(pvalue<0.05,"*",""))
ggplot(ssut,aes(x=influence,y=est.std)) +
  facet_grid(variable~region) +
  geom_errorbar(aes(ymin=ci.lower, ymax=ci.upper),width=0.5) +
  geom_point() +
  geom_text(aes(y=ci.upper+0.05, label=sig)) +
  geom_hline(yintercept = 0) +
  theme_bw() + theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1)) +
  labs(y="total effect (standardized)")

ggsave("./fig_output/uteffects.png",width = 6,height=5)

Phytoplankton-centered model (lower trophic level aggregates)

modFW='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
        chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
        potam~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modW='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
        chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
        potam~lb1*din_1+lb2*chla_1+lb3*hzoop_1+lb4*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2+lb4^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modN='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
        chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ct3*pzoop_1+ca1*flow+ca2*temp+ca3*turbid
        corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2+ct3^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modS='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
        chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ca1*flow+ca2*temp+ca3*turbid
        corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 12 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           234         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 1.413
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.842
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din ~                                                                 
##     din_1    (ns1)    0.426    0.057    7.407    0.000    0.426    0.426
##     chla     (nt1)   -0.163    0.072   -2.270    0.023   -0.163   -0.130
##     hzoop_1  (nn1)    0.030    0.060    0.499    0.618    0.030    0.030
##     pzoop_1  (nn2)   -0.022    0.058   -0.376    0.707   -0.022   -0.022
##     potam_1  (nn3)   -0.006    0.057   -0.101    0.919   -0.006   -0.006
##     flow     (na1)   -0.013    0.067   -0.189    0.850   -0.013   -0.012
##     temp     (na2)   -0.156    0.064   -2.430    0.015   -0.156   -0.149
##     turbid   (na3)   -0.138    0.068   -2.036    0.042   -0.138   -0.130
##   chla ~                                                                
##     din_1    (cb1)    0.016    0.051    0.315    0.752    0.016    0.020
##     chla_1   (cs1)    0.259    0.063    4.115    0.000    0.259    0.263
##     hzoop_1  (ct1)   -0.014    0.053   -0.276    0.783   -0.014   -0.018
##     potam_1  (ct2)   -0.018    0.050   -0.366    0.714   -0.018   -0.023
##     flow     (ca1)    0.063    0.058    1.080    0.280    0.063    0.074
##     temp     (ca2)    0.128    0.056    2.284    0.022    0.128    0.154
##     turbid   (ca3)   -0.047    0.060   -0.790    0.429   -0.047   -0.055
##   potam ~                                                               
##     chla_1   (lb1)    0.065    0.060    1.080    0.280    0.065    0.051
##     hzoop_1  (lb2)   -0.074    0.051   -1.454    0.146   -0.074   -0.072
##     pzoop_1  (lb3)   -0.163    0.050   -3.258    0.001   -0.163   -0.161
##     potam_1  (ls1)    0.629    0.048   13.019    0.000    0.629    0.629
##     flow     (la1)    0.113    0.057    1.978    0.048    0.113    0.103
##     temp     (la2)   -0.043    0.054   -0.784    0.433   -0.043   -0.040
##     turbid   (la3)    0.037    0.058    0.633    0.527    0.037    0.033
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din ~~                                                                
##    .potam            -0.045    0.044   -1.023    0.306   -0.045   -0.067
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din               0.778    0.072   10.817    0.000    0.778    0.753
##    .chla              0.602    0.056   10.817    0.000    0.602    0.908
##    .potam             0.568    0.053   10.817    0.000    0.568    0.518
## 
## R-Square:
##                    Estimate
##     din               0.247
##     chla              0.092
##     potam             0.482
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.426    0.057    7.407    0.000    0.426    0.426
##     nt                0.163    0.072    2.270    0.023    0.163    0.130
##     nn                0.037    0.061    0.615    0.539    0.037    0.038
##     na                0.209    0.072    2.887    0.004    0.209    0.198
##     cb                0.016    0.051    0.315    0.752    0.016    0.020
##     cs                0.259    0.063    4.115    0.000    0.259    0.263
##     ct                0.023    0.056    0.418    0.676    0.023    0.030
##     ca                0.150    0.058    2.612    0.009    0.150    0.179
##     lb                0.190    0.049    3.853    0.000    0.190    0.183
##     ls                0.629    0.048   13.019    0.000    0.629    0.629
##     la                0.126    0.049    2.576    0.010    0.126    0.116
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 17 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        27
##                                                       
##                                                   Used       Total
##   Number of observations                           257         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 6.565
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.087
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din ~                                                                 
##     din_1    (ns1)    0.410    0.052    7.839    0.000    0.410    0.409
##     chla     (nt1)   -0.142    0.052   -2.721    0.007   -0.142   -0.128
##     hzoop_1  (nn1)    0.008    0.052    0.161    0.872    0.008    0.009
##     pzoop_1  (nn2)    0.044    0.049    0.904    0.366    0.044    0.044
##     potam_1  (nn3)    0.076    0.053    1.439    0.150    0.076    0.077
##     flow     (na1)   -0.356    0.054   -6.551    0.000   -0.356   -0.347
##     temp     (na2)    0.026    0.047    0.557    0.577    0.026    0.026
##     turbid   (na3)    0.107    0.050    2.113    0.035    0.107    0.106
##   chla ~                                                                
##     din_1    (cb1)   -0.126    0.062   -2.019    0.043   -0.126   -0.140
##     chla_1   (cs1)    0.148    0.065    2.279    0.023    0.148    0.148
##     hzoop_1  (ct1)    0.093    0.059    1.573    0.116    0.093    0.106
##     potam_1  (ct2)    0.037    0.063    0.596    0.551    0.037    0.042
##     flow     (ca1)    0.047    0.063    0.746    0.456    0.047    0.051
##     temp     (ca2)   -0.061    0.055   -1.110    0.267   -0.061   -0.068
##     turbid   (ca3)   -0.025    0.059   -0.427    0.670   -0.025   -0.028
##   potam ~                                                               
##     din_1    (lb1)    0.074    0.044    1.692    0.091    0.074    0.073
##     chla_1   (lb2)   -0.014    0.046   -0.303    0.762   -0.014   -0.012
##     hzoop_1  (lb3)   -0.006    0.044   -0.128    0.898   -0.006   -0.006
##     pzoop_1  (lb4)    0.071    0.041    1.751    0.080    0.071    0.071
##     potam_1  (ls1)    0.708    0.044   16.008    0.000    0.708    0.702
##     flow     (la1)   -0.112    0.045   -2.497    0.013   -0.112   -0.108
##     temp     (la2)   -0.009    0.039   -0.223    0.823   -0.009   -0.009
##     turbid   (la3)   -0.079    0.042   -1.895    0.058   -0.079   -0.078
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din ~~                                                                
##    .potam             0.037    0.027    1.379    0.168    0.037    0.086
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din               0.523    0.046   11.336    0.000    0.523    0.525
##    .chla              0.726    0.064   11.336    0.000    0.726    0.900
##    .potam             0.359    0.032   11.336    0.000    0.359    0.351
## 
## R-Square:
##                    Estimate
##     din               0.475
##     chla              0.100
##     potam             0.649
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.410    0.052    7.839    0.000    0.410    0.409
##     nt                0.142    0.052    2.721    0.007    0.142    0.128
##     nn                0.089    0.053    1.666    0.096    0.089    0.089
##     na                0.373    0.057    6.577    0.000    0.373    0.364
##     cb                0.126    0.062    2.019    0.043    0.126    0.140
##     cs                0.148    0.065    2.279    0.023    0.148    0.148
##     ct                0.100    0.064    1.550    0.121    0.100    0.114
##     ca                0.081    0.061    1.330    0.184    0.081    0.090
##     lb                0.104    0.041    2.519    0.012    0.104    0.103
##     ls                0.708    0.044   16.008    0.000    0.708    0.702
##     la                0.138    0.040    3.423    0.001    0.138    0.134
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 11 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        27
##                                                       
##                                                   Used       Total
##   Number of observations                           255         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 5.384
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.146
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din ~                                                                 
##     din_1    (ns1)    0.129    0.053    2.407    0.016    0.129    0.130
##     chla     (nt1)   -0.173    0.050   -3.491    0.000   -0.173   -0.175
##     hzoop_1  (nn1)    0.048    0.052    0.920    0.358    0.048    0.048
##     pzoop_1  (nn2)    0.176    0.054    3.249    0.001    0.176    0.174
##     corbic_1 (nn3)   -0.027    0.051   -0.523    0.601   -0.027   -0.026
##     flow     (na1)   -0.468    0.057   -8.165    0.000   -0.468   -0.459
##     temp     (na2)    0.027    0.051    0.534    0.594    0.027    0.027
##     turbid   (na3)    0.056    0.049    1.139    0.255    0.056    0.057
##   chla ~                                                                
##     din_1    (cb1)    0.025    0.066    0.380    0.704    0.025    0.025
##     chla_1   (cs1)    0.279    0.060    4.645    0.000    0.279    0.281
##     hzoop_1  (ct1)   -0.050    0.063   -0.804    0.421   -0.050   -0.050
##     corbic_1 (ct2)    0.017    0.062    0.267    0.789    0.017    0.016
##     pzoop_1  (ct3)   -0.169    0.066   -2.573    0.010   -0.169   -0.166
##     flow     (ca1)   -0.054    0.069   -0.782    0.434   -0.054   -0.053
##     temp     (ca2)    0.136    0.061    2.218    0.027    0.136    0.136
##     turbid   (ca3)    0.077    0.059    1.298    0.194    0.077    0.078
##   corbic ~                                                              
##     chla_1   (lb1)    0.003    0.053    0.057    0.954    0.003    0.003
##     hzoop_1  (lb2)    0.011    0.056    0.194    0.846    0.011    0.011
##     pzoop_1  (lb3)   -0.011    0.058   -0.185    0.853   -0.011   -0.011
##     corbic_1 (ls1)    0.463    0.056    8.312    0.000    0.463    0.460
##     flow     (la1)    0.093    0.060    1.552    0.121    0.093    0.092
##     temp     (la2)   -0.034    0.055   -0.614    0.539   -0.034   -0.034
##     turbid   (la3)   -0.117    0.053   -2.212    0.027   -0.117   -0.121
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din ~~                                                                
##    .corbic           -0.017    0.041   -0.418    0.676   -0.017   -0.026
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din               0.602    0.053   11.292    0.000    0.602    0.610
##    .chla              0.882    0.078   11.292    0.000    0.882    0.879
##    .corbic            0.715    0.063   11.292    0.000    0.715    0.740
## 
## R-Square:
##                    Estimate
##     din               0.390
##     chla              0.121
##     corbic            0.260
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.129    0.053    2.407    0.016    0.129    0.130
##     nt                0.173    0.050    3.491    0.000    0.173    0.175
##     nn                0.185    0.051    3.645    0.000    0.185    0.182
##     na                0.473    0.058    8.206    0.000    0.473    0.463
##     cb                0.025    0.066    0.380    0.704    0.025    0.025
##     cs                0.279    0.060    4.645    0.000    0.279    0.281
##     ct                0.177    0.061    2.899    0.004    0.177    0.174
##     ca                0.165    0.061    2.726    0.006    0.165    0.165
##     lb                0.016    0.063    0.248    0.804    0.016    0.016
##     ls                0.463    0.056    8.312    0.000    0.463    0.460
##     la                0.153    0.057    2.689    0.007    0.153    0.156
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 10 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           256         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 2.680
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.613
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din ~                                                                 
##     din_1    (ns1)    0.203    0.061    3.318    0.001    0.203    0.204
##     chla     (nt1)    0.026    0.059    0.445    0.656    0.026    0.026
##     hzoop_1  (nn1)   -0.026    0.064   -0.403    0.687   -0.026   -0.024
##     pzoop_1  (nn2)    0.028    0.064    0.438    0.662    0.028    0.026
##     corbic_1 (nn3)    0.073    0.060    1.215    0.224    0.073    0.073
##     flow     (na1)   -0.005    0.061   -0.088    0.930   -0.005   -0.005
##     temp     (na2)    0.096    0.060    1.610    0.107    0.096    0.095
##     turbid   (na3)    0.252    0.063    3.997    0.000    0.252    0.249
##   chla ~                                                                
##     din_1    (cb1)   -0.007    0.062   -0.120    0.905   -0.007   -0.008
##     chla_1   (cs1)    0.285    0.061    4.690    0.000    0.285    0.285
##     hzoop_1  (ct1)    0.075    0.064    1.165    0.244    0.075    0.072
##     corbic_1 (ct2)    0.086    0.060    1.429    0.153    0.086    0.087
##     flow     (ca1)   -0.118    0.061   -1.931    0.053   -0.118   -0.116
##     temp     (ca2)    0.020    0.059    0.341    0.733    0.020    0.020
##     turbid   (ca3)    0.017    0.064    0.263    0.793    0.017    0.017
##   corbic ~                                                              
##     chla_1   (lb1)    0.053    0.059    0.899    0.369    0.053    0.053
##     hzoop_1  (lb2)   -0.024    0.062   -0.392    0.695   -0.024   -0.023
##     pzoop_1  (lb3)   -0.041    0.063   -0.658    0.511   -0.041   -0.039
##     corbic_1 (ls1)    0.315    0.058    5.477    0.000    0.315    0.318
##     flow     (la1)    0.082    0.059    1.396    0.163    0.082    0.081
##     temp     (la2)   -0.078    0.058   -1.352    0.176   -0.078   -0.078
##     turbid   (la3)    0.185    0.058    3.195    0.001    0.185    0.185
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din ~~                                                                
##    .corbic           -0.074    0.053   -1.403    0.161   -0.074   -0.088
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din               0.869    0.077   11.314    0.000    0.869    0.845
##    .chla              0.880    0.078   11.314    0.000    0.880    0.881
##    .corbic            0.812    0.072   11.314    0.000    0.812    0.809
## 
## R-Square:
##                    Estimate
##     din               0.155
##     chla              0.119
##     corbic            0.191
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.203    0.061    3.318    0.001    0.203    0.204
##     nt                0.026    0.059    0.445    0.656    0.026    0.026
##     nn                0.082    0.064    1.293    0.196    0.082    0.081
##     na                0.269    0.064    4.219    0.000    0.269    0.267
##     cb                0.007    0.062    0.120    0.905    0.007    0.008
##     cs                0.285    0.061    4.690    0.000    0.285    0.285
##     ct                0.114    0.059    1.927    0.054    0.114    0.113
##     ca                0.120    0.062    1.955    0.051    0.120    0.119
##     lb                0.072    0.066    1.087    0.277    0.072    0.070
##     ls                0.315    0.058    5.477    0.000    0.315    0.318
##     la                0.217    0.055    3.916    0.000    0.217    0.216
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-bind_rows(coef_table, coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Lower trophic level aggregates"))
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
lower_plot_far_west <- createGraph(fit=modfitFW, 
                                   reference_df=cnameslag, 
                                   model_type="monthly_lower_trophic",
                                   title="Far West",
                                   manual_port_settings=TRUE)
lower_plot_far_west
#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
lower_plot_west <- createGraph(fit=modfitW, 
                               reference_df=cnameslag, 
                               model_type="monthly_lower_trophic",
                               title="West",
                               manual_port_settings=TRUE)
lower_plot_west
#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
lower_plot_north <- createGraph(fit=modfitN, 
                                reference_df=cnameslag, 
                                model_type="monthly_lower_trophic",
                                title="North",
                                manual_port_settings=TRUE)
lower_plot_north
#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
lower_plot_south <- createGraph(fit=modfitS, 
                                reference_df=cnameslag, 
                                model_type="monthly_lower_trophic",
                                title="South",
                                manual_port_settings=TRUE)
lower_plot_south

Save updated SEM diagrams

Total effects

ssFW=standardizedsolution(modfitFW) %>% mutate(region="Far West")
ssW=standardizedsolution(modfitW) %>% mutate(region="West")
ssN=standardizedsolution(modfitN) %>% mutate(region="North")
ssS=standardizedsolution(modfitS) %>% mutate(region="South")
sslt=rbind(ssFW,ssW,ssN,ssS) %>% filter(op==":=") %>% select(region,lhs,est.std:ci.upper) %>% 
  separate(lhs,c("variable","influence"), sep=1) %>% 
  mutate(variable=case_when(variable=="n" ~ "DIN",
                            variable=="c" ~ "phytoplankton",
                            variable=="l" ~ "clams"),
         influence=case_when(influence=="b" ~ "bottom-up",
                            influence=="t" ~ "top-down",
                            influence=="s" ~ "self-regulation",
                            influence=="a" ~ "abiotic drivers",
                            influence=="n" ~ "nutrient cycling"),
         region=factor(region, levels=regionorder),
         influence=factor(influence, levels=c("self-regulation","bottom-up","top-down","abiotic drivers","nutrient cycling")),
         variable=factor(variable,levels=c("clams","phytoplankton","DIN")),
         sig=ifelse(pvalue<0.05,"*",""))
ggplot(sslt,aes(x=influence,y=est.std)) +
  facet_grid(variable~region) +
  geom_errorbar(aes(ymin=ci.lower, ymax=ci.upper),width=0.5) +
  geom_point() +
  geom_text(aes(y=ci.upper+0.05, label=sig)) +
  geom_hline(yintercept = 0) +
  theme_bw() + theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1)) +
  labs(y="total effect (standardized)")

ggsave("./fig_output/lteffects.png",width = 6,height=5)

Zooplankton-centered model (individual groups)

#1
# modFW='chla~chla_1+hcope_1+amphi_m_1+potam_1+flow+turbid+temp
#        hcope~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
#        amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
#        pcope~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
# '
# modW='chla~chla_1+hcope_1+amphi_m_1+potam_1+flow+turbid+temp+mysid_1
#        hcope~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1
#        amphi_m~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
#        pcope~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1
#        mysid~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
# '
# modN='chla~chla_1+hcope_1+amphi_m_1+corbic_1+flow+turbid+temp
#        hcope~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
#        amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
#        pcope~hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
#        mysid~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
# '
# modS='chla~chla_1+hcope_1+clad_1+corbic_1+flow+turbid+temp
#        hcope~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
#        clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
#        amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
#        pcope~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
# '
#2
modFW='chla~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp
       hcope~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
'
modW='chla~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp+mysid_1
       hcope~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
       amphi_m~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
       mysid~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
'
modN='chla~chla_1+hcope_1+clad_1+amphi_m_1+rotif_m_1+corbic_1+flow+turbid+temp
       hcope~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
       clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope~hcope_1+pcope_1+clad_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1+chla_1
       mysid~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
'
modS='chla~chla_1+hcope_1+clad_1+rotif_m_1+corbic_1+flow+turbid+temp
       hcope~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
       clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 26 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        50
##                                                       
##                                                   Used       Total
##   Number of observations                           192         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                18.154
##   Degrees of freedom                                15
##   P-value (Chi-square)                           0.255
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla ~                                                                
##     chla_1            0.208    0.068    3.065    0.002    0.208    0.212
##     hcope_1           0.066    0.055    1.199    0.230    0.066    0.084
##     amphi_m_1         0.026    0.067    0.383    0.702    0.026    0.032
##     rotif_m_1        -0.069    0.063   -1.096    0.273   -0.069   -0.080
##     potam_1          -0.003    0.050   -0.064    0.949   -0.003   -0.004
##     flow              0.121    0.073    1.674    0.094    0.121    0.144
##     turbid           -0.078    0.069   -1.131    0.258   -0.078   -0.088
##     temp              0.138    0.062    2.238    0.025    0.138    0.161
##   hcope ~                                                               
##     chla_1            0.106    0.082    1.297    0.195    0.106    0.085
##     hcope_1           0.234    0.070    3.317    0.001    0.234    0.234
##     pcope_1           0.066    0.072    0.915    0.360    0.066    0.064
##     potam_1          -0.146    0.063   -2.296    0.022   -0.146   -0.158
##     flow             -0.073    0.083   -0.882    0.378   -0.073   -0.068
##     turbid           -0.016    0.084   -0.185    0.853   -0.016   -0.014
##     temp             -0.055    0.077   -0.714    0.475   -0.055   -0.050
##     estfish_bsmt_1   -0.133    0.076   -1.744    0.081   -0.133   -0.130
##   amphi_m ~                                                             
##     chla_1            0.051    0.039    1.293    0.196    0.051    0.042
##     amphi_m_1         0.730    0.038   19.451    0.000    0.730    0.745
##     flow             -0.261    0.042   -6.188    0.000   -0.261   -0.248
##     turbid           -0.011    0.040   -0.290    0.772   -0.011   -0.010
##     temp              0.014    0.036    0.385    0.700    0.014    0.013
##     estfish_bsmt_1    0.042    0.034    1.214    0.225    0.042    0.042
##   rotif_m ~                                                             
##     chla_1           -0.198    0.075   -2.635    0.008   -0.198   -0.173
##     rotif_m_1         0.382    0.066    5.764    0.000    0.382    0.379
##     flow              0.102    0.074    1.372    0.170    0.102    0.103
##     turbid            0.007    0.076    0.099    0.922    0.007    0.007
##     temp              0.055    0.069    0.799    0.424    0.055    0.055
##     estfish_bsmt_1    0.008    0.065    0.130    0.897    0.008    0.009
##   pcope ~                                                               
##     hcope_1           0.044    0.065    0.680    0.497    0.044    0.047
##     pcope_1           0.299    0.067    4.464    0.000    0.299    0.303
##     potam_1          -0.093    0.059   -1.580    0.114   -0.093   -0.105
##     flow              0.206    0.076    2.704    0.007    0.206    0.201
##     turbid            0.087    0.078    1.115    0.265    0.087    0.081
##     temp              0.159    0.072    2.220    0.026    0.159    0.152
##     estfish_bsmt_1   -0.007    0.070   -0.096    0.923   -0.007   -0.007
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla ~~                                                               
##    .hcope             0.036    0.054    0.669    0.504    0.036    0.048
##    .amphi_m           0.017    0.025    0.689    0.491    0.017    0.050
##    .rotif_m           0.071    0.048    1.469    0.142    0.071    0.107
##    .pcope            -0.006    0.050   -0.126    0.900   -0.006   -0.009
##  .hcope ~~                                                              
##    .amphi_m          -0.012    0.031   -0.387    0.699   -0.012   -0.028
##    .rotif_m           0.037    0.060    0.618    0.537    0.037    0.045
##    .pcope            -0.202    0.063   -3.193    0.001   -0.202   -0.237
##  .amphi_m ~~                                                            
##    .rotif_m          -0.032    0.028   -1.131    0.258   -0.032   -0.082
##    .pcope            -0.008    0.029   -0.272    0.785   -0.008   -0.020
##  .rotif_m ~~                                                            
##    .pcope             0.049    0.055    0.884    0.377    0.049    0.064
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla              0.600    0.061    9.798    0.000    0.600    0.892
##    .hcope             0.923    0.094    9.798    0.000    0.923    0.850
##    .amphi_m           0.201    0.021    9.798    0.000    0.201    0.193
##    .rotif_m           0.741    0.076    9.798    0.000    0.741    0.807
##    .pcope             0.792    0.081    9.798    0.000    0.792    0.801
## 
## R-Square:
##                    Estimate
##     chla              0.108
##     hcope             0.150
##     amphi_m           0.807
##     rotif_m           0.193
##     pcope             0.199
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 27 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        71
##                                                       
##                                                   Used       Total
##   Number of observations                           215         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                28.044
##   Degrees of freedom                                16
##   P-value (Chi-square)                           0.031
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla ~                                                                
##     chla_1            0.155    0.068    2.281    0.023    0.155    0.157
##     hcope_1           0.058    0.064    0.898    0.369    0.058    0.063
##     amphi_m_1         0.139    0.061    2.279    0.023    0.139    0.161
##     rotif_m_1         0.087    0.057    1.514    0.130    0.087    0.103
##     potam_1          -0.049    0.066   -0.748    0.455   -0.049   -0.055
##     flow              0.068    0.067    1.003    0.316    0.068    0.072
##     turbid            0.074    0.068    1.084    0.278    0.074    0.080
##     temp             -0.095    0.059   -1.606    0.108   -0.095   -0.106
##     mysid_1          -0.157    0.065   -2.430    0.015   -0.157   -0.172
##   hcope ~                                                               
##     chla_1            0.048    0.070    0.683    0.494    0.048    0.045
##     hcope_1           0.306    0.068    4.482    0.000    0.306    0.307
##     pcope_1          -0.096    0.060   -1.591    0.112   -0.096   -0.102
##     mysid_1           0.048    0.070    0.687    0.492    0.048    0.049
##     potam_1          -0.166    0.065   -2.531    0.011   -0.166   -0.172
##     flow             -0.137    0.071   -1.933    0.053   -0.137   -0.135
##     turbid           -0.119    0.071   -1.673    0.094   -0.119   -0.119
##     temp              0.063    0.062    1.017    0.309    0.063    0.065
##     estfish_bsmt_1    0.048    0.060    0.807    0.420    0.048    0.051
##     rotif_m_1         0.173    0.059    2.938    0.003    0.173    0.190
##   amphi_m ~                                                             
##     chla_1           -0.047    0.041   -1.154    0.248   -0.047   -0.041
##     amphi_m_1         0.783    0.038   20.620    0.000    0.783    0.787
##     mysid_1           0.080    0.039    2.074    0.038    0.080    0.076
##     flow              0.008    0.040    0.196    0.844    0.008    0.007
##     turbid           -0.109    0.041   -2.632    0.008   -0.109   -0.103
##     temp             -0.103    0.037   -2.790    0.005   -0.103   -0.099
##     estfish_bsmt_1   -0.177    0.037   -4.761    0.000   -0.177   -0.176
##   rotif_m ~                                                             
##     chla_1            0.038    0.072    0.527    0.598    0.038    0.032
##     rotif_m_1         0.333    0.063    5.303    0.000    0.333    0.332
##     flow              0.239    0.072    3.307    0.001    0.239    0.214
##     turbid           -0.105    0.070   -1.495    0.135   -0.105   -0.095
##     temp              0.032    0.065    0.491    0.623    0.032    0.030
##     estfish_bsmt_1   -0.189    0.062   -3.062    0.002   -0.189   -0.182
##   pcope ~                                                               
##     hcope_1          -0.130    0.060   -2.167    0.030   -0.130   -0.125
##     pcope_1           0.393    0.055    7.137    0.000    0.393    0.405
##     mysid_1           0.118    0.063    1.864    0.062    0.118    0.115
##     potam_1           0.046    0.061    0.762    0.446    0.046    0.046
##     flow              0.138    0.064    2.163    0.031    0.138    0.131
##     turbid           -0.144    0.065   -2.225    0.026   -0.144   -0.138
##     temp              0.229    0.055    4.133    0.000    0.229    0.227
##     estfish_bsmt_1    0.026    0.056    0.470    0.639    0.026    0.027
##     rotif_m_1         0.161    0.054    2.988    0.003    0.161    0.169
##   mysid ~                                                               
##     chla_1            0.181    0.062    2.910    0.004    0.181    0.169
##     hcope_1           0.073    0.060    1.217    0.224    0.073    0.073
##     pcope_1           0.110    0.053    2.054    0.040    0.110    0.116
##     amphi_m_1        -0.123    0.056   -2.197    0.028   -0.123   -0.131
##     mysid_1           0.372    0.063    5.929    0.000    0.372    0.373
##     flow             -0.168    0.060   -2.788    0.005   -0.168   -0.165
##     turbid            0.257    0.064    4.032    0.000    0.257    0.255
##     temp              0.097    0.055    1.755    0.079    0.097    0.100
##     estfish_bsmt_1   -0.023    0.056   -0.423    0.673   -0.023   -0.025
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla ~~                                                               
##    .hcope             0.150    0.050    3.019    0.003    0.150    0.210
##    .amphi_m          -0.002    0.029   -0.056    0.956   -0.002   -0.004
##    .rotif_m           0.125    0.052    2.386    0.017    0.125    0.165
##    .pcope             0.015    0.044    0.337    0.736    0.015    0.023
##    .mysid             0.078    0.044    1.788    0.074    0.078    0.123
##  .hcope ~~                                                              
##    .amphi_m          -0.036    0.030   -1.195    0.232   -0.036   -0.082
##    .rotif_m          -0.015    0.054   -0.276    0.782   -0.015   -0.019
##    .pcope             0.080    0.046    1.718    0.086    0.080    0.118
##    .mysid             0.204    0.048    4.285    0.000    0.204    0.306
##  .amphi_m ~~                                                            
##    .rotif_m           0.043    0.032    1.328    0.184    0.043    0.091
##    .pcope             0.068    0.028    2.438    0.015    0.068    0.169
##    .mysid            -0.039    0.027   -1.420    0.156   -0.039   -0.097
##  .rotif_m ~~                                                            
##    .pcope             0.068    0.049    1.386    0.166    0.068    0.095
##    .mysid             0.029    0.048    0.598    0.550    0.029    0.041
##  .pcope ~~                                                              
##    .mysid             0.056    0.041    1.344    0.179    0.056    0.092
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla              0.682    0.066   10.368    0.000    0.682    0.845
##    .hcope             0.747    0.072   10.368    0.000    0.747    0.791
##    .amphi_m           0.266    0.026   10.368    0.000    0.266    0.249
##    .rotif_m           0.838    0.081   10.368    0.000    0.838    0.735
##    .pcope             0.613    0.059   10.368    0.000    0.613    0.599
##    .mysid             0.598    0.058   10.368    0.000    0.598    0.625
## 
## R-Square:
##                    Estimate
##     chla              0.155
##     hcope             0.209
##     amphi_m           0.751
##     rotif_m           0.265
##     pcope             0.401
##     mysid             0.375
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 31 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        83
##                                                       
##                                                   Used       Total
##   Number of observations                           205         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                35.639
##   Degrees of freedom                                29
##   P-value (Chi-square)                           0.184
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla ~                                                                
##     chla_1            0.168    0.074    2.267    0.023    0.168    0.155
##     hcope_1          -0.017    0.092   -0.180    0.857   -0.017   -0.014
##     clad_1            0.188    0.078    2.424    0.015    0.188    0.185
##     amphi_m_1         0.063    0.072    0.868    0.385    0.063    0.059
##     rotif_m_1        -0.040    0.070   -0.569    0.569   -0.040   -0.041
##     corbic_1         -0.026    0.071   -0.368    0.713   -0.026   -0.025
##     flow             -0.034    0.079   -0.435    0.663   -0.034   -0.033
##     turbid            0.095    0.071    1.332    0.183    0.095    0.095
##     temp              0.119    0.070    1.701    0.089    0.119    0.120
##   hcope ~                                                               
##     chla_1           -0.009    0.043   -0.212    0.832   -0.009   -0.011
##     hcope_1           0.121    0.068    1.778    0.075    0.121    0.128
##     pcope_1          -0.095    0.043   -2.198    0.028   -0.095   -0.131
##     mysid_1           0.046    0.060    0.767    0.443    0.046    0.058
##     corbic_1          0.102    0.042    2.433    0.015    0.102    0.125
##     flow             -0.394    0.049   -8.053    0.000   -0.394   -0.498
##     turbid            0.089    0.047    1.888    0.059    0.089    0.115
##     temp              0.097    0.044    2.220    0.026    0.097    0.127
##     estfish_bsmt_1    0.007    0.044    0.167    0.867    0.007    0.010
##   clad ~                                                                
##     chla_1           -0.037    0.062   -0.600    0.549   -0.037   -0.033
##     clad_1            0.377    0.063    6.024    0.000    0.377    0.356
##     pcope_1           0.016    0.057    0.275    0.783    0.016    0.016
##     flow              0.457    0.067    6.794    0.000    0.457    0.428
##     turbid            0.003    0.062    0.056    0.955    0.003    0.003
##     temp              0.051    0.058    0.882    0.378    0.051    0.050
##     estfish_bsmt_1    0.019    0.058    0.333    0.739    0.019    0.020
##   amphi_m ~                                                             
##     chla_1            0.059    0.058    1.011    0.312    0.059    0.060
##     amphi_m_1         0.499    0.057    8.740    0.000    0.499    0.514
##     flow              0.053    0.061    0.863    0.388    0.053    0.056
##     turbid           -0.020    0.056   -0.346    0.730   -0.020   -0.021
##     temp             -0.039    0.055   -0.710    0.477   -0.039   -0.043
##     estfish_bsmt_1   -0.068    0.055   -1.227    0.220   -0.068   -0.079
##   rotif_m ~                                                             
##     chla_1           -0.089    0.070   -1.281    0.200   -0.089   -0.079
##     rotif_m_1         0.155    0.064    2.426    0.015    0.155    0.152
##     flow              0.405    0.073    5.531    0.000    0.405    0.381
##     turbid           -0.052    0.067   -0.777    0.437   -0.052   -0.050
##     temp             -0.076    0.065   -1.164    0.244   -0.076   -0.074
##     estfish_bsmt_1   -0.015    0.065   -0.225    0.822   -0.015   -0.015
##   pcope ~                                                               
##     hcope_1          -0.146    0.102   -1.431    0.153   -0.146   -0.115
##     pcope_1           0.251    0.065    3.885    0.000    0.251    0.256
##     clad_1           -0.134    0.070   -1.909    0.056   -0.134   -0.128
##     mysid_1           0.064    0.090    0.708    0.479    0.064    0.060
##     corbic_1          0.064    0.066    0.967    0.333    0.064    0.059
##     flow             -0.193    0.076   -2.542    0.011   -0.193   -0.182
##     turbid           -0.244    0.071   -3.439    0.001   -0.244   -0.235
##     temp              0.072    0.066    1.094    0.274    0.072    0.071
##     estfish_bsmt_1   -0.063    0.067   -0.943    0.346   -0.063   -0.064
##     chla_1            0.153    0.070    2.197    0.028    0.153    0.137
##   mysid ~                                                               
##     hcope_1          -0.021    0.084   -0.253    0.800   -0.021   -0.018
##     pcope_1          -0.061    0.054   -1.132    0.258   -0.061   -0.067
##     mysid_1           0.173    0.073    2.360    0.018    0.173    0.175
##     amphi_m_1        -0.081    0.052   -1.565    0.118   -0.081   -0.080
##     flow             -0.420    0.062   -6.785    0.000   -0.420   -0.427
##     turbid            0.228    0.060    3.809    0.000    0.228    0.236
##     temp              0.081    0.054    1.483    0.138    0.081    0.085
##     estfish_bsmt_1    0.056    0.056    1.011    0.312    0.056    0.062
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla ~~                                                               
##    .hcope             0.022    0.040    0.543    0.587    0.022    0.038
##    .clad              0.140    0.056    2.521    0.012    0.140    0.179
##    .amphi_m           0.018    0.052    0.355    0.723    0.018    0.025
##    .rotif_m           0.098    0.062    1.582    0.114    0.098    0.111
##    .pcope            -0.058    0.061   -0.959    0.338   -0.058   -0.067
##    .mysid             0.084    0.051    1.636    0.102    0.084    0.115
##  .hcope ~~                                                              
##    .clad             -0.059    0.035   -1.722    0.085   -0.059   -0.121
##    .amphi_m           0.039    0.033    1.183    0.237    0.039    0.083
##    .rotif_m          -0.007    0.038   -0.191    0.849   -0.007   -0.013
##    .pcope             0.060    0.038    1.563    0.118    0.060    0.110
##    .mysid             0.180    0.034    5.257    0.000    0.180    0.395
##  .clad ~~                                                               
##    .amphi_m           0.004    0.044    0.088    0.930    0.004    0.006
##    .rotif_m           0.107    0.053    2.016    0.044    0.107    0.142
##    .pcope            -0.051    0.052   -0.991    0.322   -0.051   -0.069
##    .mysid            -0.083    0.044   -1.899    0.058   -0.083   -0.134
##  .amphi_m ~~                                                            
##    .rotif_m          -0.128    0.051   -2.529    0.011   -0.128   -0.179
##    .pcope            -0.088    0.049   -1.777    0.076   -0.088   -0.125
##    .mysid             0.088    0.042    2.103    0.035    0.088    0.148
##  .rotif_m ~~                                                            
##    .pcope             0.150    0.059    2.552    0.011    0.150    0.181
##    .mysid             0.002    0.049    0.045    0.964    0.002    0.003
##  .pcope ~~                                                              
##    .mysid             0.135    0.049    2.757    0.006    0.135    0.196
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla              0.921    0.091   10.124    0.000    0.921    0.908
##    .hcope             0.360    0.036   10.124    0.000    0.360    0.595
##    .clad              0.669    0.066   10.124    0.000    0.669    0.608
##    .amphi_m           0.603    0.060   10.124    0.000    0.603    0.713
##    .rotif_m           0.842    0.083   10.124    0.000    0.842    0.773
##    .pcope             0.819    0.081   10.124    0.000    0.819    0.755
##    .mysid             0.577    0.057   10.124    0.000    0.577    0.619
## 
## R-Square:
##                    Estimate
##     chla              0.092
##     hcope             0.405
##     clad              0.392
##     amphi_m           0.287
##     rotif_m           0.227
##     pcope             0.245
##     mysid             0.381
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        65
##                                                       
##                                                   Used       Total
##   Number of observations                           210         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                24.647
##   Degrees of freedom                                22
##   P-value (Chi-square)                           0.314
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla ~                                                                
##     chla_1            0.227    0.070    3.227    0.001    0.227    0.220
##     hcope_1           0.116    0.068    1.706    0.088    0.116    0.109
##     clad_1            0.177    0.067    2.633    0.008    0.177    0.181
##     rotif_m_1        -0.074    0.068   -1.088    0.277   -0.074   -0.069
##     corbic_1         -0.008    0.065   -0.126    0.900   -0.008   -0.008
##     flow             -0.076    0.074   -1.018    0.309   -0.076   -0.069
##     turbid            0.016    0.067    0.243    0.808    0.016    0.016
##     temp              0.073    0.066    1.114    0.265    0.073    0.072
##   hcope ~                                                               
##     chla_1            0.159    0.058    2.763    0.006    0.159    0.163
##     hcope_1           0.357    0.061    5.834    0.000    0.357    0.355
##     pcope_1           0.001    0.056    0.012    0.991    0.001    0.001
##     corbic_1          0.114    0.058    1.975    0.048    0.114    0.117
##     flow             -0.143    0.063   -2.280    0.023   -0.143   -0.139
##     turbid           -0.093    0.058   -1.614    0.106   -0.093   -0.098
##     temp              0.179    0.057    3.128    0.002    0.179    0.187
##     estfish_bsmt_1   -0.182    0.058   -3.144    0.002   -0.182   -0.189
##   clad ~                                                                
##     chla_1            0.143    0.059    2.430    0.015    0.143    0.137
##     clad_1            0.516    0.058    8.883    0.000    0.516    0.517
##     pcope_1          -0.028    0.053   -0.519    0.604   -0.028   -0.028
##     flow              0.200    0.061    3.282    0.001    0.200    0.181
##     turbid           -0.050    0.057   -0.873    0.382   -0.050   -0.049
##     temp              0.110    0.056    1.953    0.051    0.110    0.107
##     estfish_bsmt_1   -0.093    0.055   -1.690    0.091   -0.093   -0.090
##   amphi_m ~                                                             
##     chla_1           -0.057    0.071   -0.808    0.419   -0.057   -0.053
##     amphi_m_1         0.218    0.068    3.202    0.001    0.218    0.214
##     flow             -0.008    0.075   -0.101    0.920   -0.008   -0.007
##     turbid            0.169    0.071    2.370    0.018    0.169    0.162
##     temp              0.092    0.070    1.301    0.193    0.092    0.087
##     estfish_bsmt_1    0.049    0.072    0.687    0.492    0.049    0.047
##   rotif_m ~                                                             
##     chla_1            0.036    0.065    0.553    0.580    0.036    0.036
##     rotif_m_1         0.119    0.066    1.793    0.073    0.119    0.115
##     flow              0.323    0.070    4.611    0.000    0.323    0.304
##     turbid           -0.071    0.064   -1.102    0.270   -0.071   -0.072
##     temp              0.001    0.065    0.021    0.983    0.001    0.001
##     estfish_bsmt_1    0.171    0.065    2.614    0.009    0.171    0.173
##   pcope ~                                                               
##     chla_1            0.237    0.065    3.639    0.000    0.237    0.225
##     hcope_1          -0.059    0.064   -0.926    0.355   -0.059   -0.055
##     clad_1            0.071    0.063    1.133    0.257    0.071    0.071
##     pcope_1           0.443    0.060    7.356    0.000    0.443    0.444
##     corbic_1         -0.054    0.061   -0.886    0.376   -0.054   -0.051
##     flow              0.033    0.069    0.472    0.637    0.033    0.029
##     turbid           -0.080    0.063   -1.257    0.209   -0.080   -0.078
##     temp              0.011    0.062    0.181    0.856    0.011    0.011
##     estfish_bsmt_1   -0.051    0.063   -0.811    0.418   -0.051   -0.049
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla ~~                                                               
##    .hcope             0.091    0.052    1.742    0.082    0.091    0.121
##    .clad              0.212    0.053    3.980    0.000    0.212    0.286
##    .amphi_m           0.004    0.064    0.064    0.949    0.004    0.004
##    .rotif_m           0.122    0.060    2.044    0.041    0.122    0.142
##    .pcope            -0.018    0.056   -0.324    0.746   -0.018   -0.022
##  .hcope ~~                                                              
##    .clad              0.046    0.044    1.060    0.289    0.046    0.073
##    .amphi_m           0.026    0.055    0.478    0.633    0.026    0.033
##    .rotif_m           0.015    0.051    0.288    0.773    0.015    0.020
##    .pcope             0.027    0.048    0.566    0.571    0.027    0.039
##  .clad ~~                                                               
##    .amphi_m          -0.095    0.055   -1.734    0.083   -0.095   -0.120
##    .rotif_m           0.032    0.050    0.630    0.528    0.032    0.044
##    .pcope             0.082    0.048    1.728    0.084    0.082    0.120
##  .amphi_m ~~                                                            
##    .rotif_m           0.059    0.063    0.942    0.346    0.059    0.065
##    .pcope             0.035    0.060    0.588    0.556    0.035    0.041
##  .rotif_m ~~                                                            
##    .pcope             0.199    0.057    3.512    0.000    0.199    0.250
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla              0.880    0.086   10.247    0.000    0.880    0.862
##    .hcope             0.642    0.063   10.247    0.000    0.642    0.703
##    .clad              0.625    0.061   10.247    0.000    0.625    0.593
##    .amphi_m           0.989    0.097   10.247    0.000    0.989    0.903
##    .rotif_m           0.838    0.082   10.247    0.000    0.838    0.866
##    .pcope             0.754    0.074   10.247    0.000    0.754    0.710
## 
## R-Square:
##                    Estimate
##     chla              0.138
##     hcope             0.297
##     clad              0.407
##     amphi_m           0.097
##     rotif_m           0.134
##     pcope             0.290
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-bind_rows(coef_table, coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Individual zooplankton groups"))
write.csv(coef_table, "fig_output/monthly coefficients.csv", row.names = FALSE)
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
zoop_plot_far_west <- createGraph(fit=modfitFW, 
                                  reference_df=cnameslag, 
                                  model_type="monthly_zoop",
                                  region="Far West",
                                  title="Far West",
                                  manual_port_settings=TRUE)
zoop_plot_far_west
#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
zoop_plot_west <- createGraph(fit=modfitW, 
                              reference_df=cnameslag, 
                              model_type="monthly_zoop",
                              region="West",
                              title="West",
                              manual_port_settings=TRUE)
zoop_plot_west
#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
zoop_plot_north <- createGraph(fit=modfitN, 
                               reference_df=cnameslag, 
                               model_type="monthly_zoop",
                               region="North",
                               title="North",
                               manual_port_settings=TRUE)
zoop_plot_north
#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
#                        node_options=list(shape="box", fontname="Helvetica"), 
#                        coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05, 
#                        width=c("regress","latent"),
#                        color=c("regress","latent"))
zoop_plot_south <- createGraph(fit=modfitS, 
                               reference_df=cnameslag, 
                               model_type="monthly_zoop",
                               region="South",
                               title="South",
                               manual_port_settings=TRUE)
zoop_plot_south

Save updated SEM diagrams

zoop_grobs <-  map(list(zoop_plot_far_west,
                        zoop_plot_west,
                        zoop_plot_north,
                        zoop_plot_south), ~convert_html_to_grob(.x, 2000))
zoop_figure <- ggarrange(plotlist=zoop_grobs, labels="auto",
                         font.label=list(size=11)) %>%
  annotate_figure(top = text_grob("Monthly Regional Models (zooplankton groups)",
                                  color = "black",
                                  face = "bold",
                                  size = 11))
ggsave('./fig_output/sem_zoop.png',zoop_figure, width=8, height=7, dpi=300, bg = "white")

Total effects

Haven’t done yet.

Growth rates

Upper trophic

modFW='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop_gr~pb1*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*marfish_bsmt_1+ft2*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modW='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modN='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2+ft3^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modS='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
        pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
        estfish_bsmt_gr~fb1*chla_1+fb2*hzoop_1+fb3*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
        
        hb:=sqrt(hb1^2)
        hs:=sqrt(hs1^2)
        ht:=sqrt(ht1^2+ht2^2+ht3^2)
        ha:=sqrt(ha1^2+ha2^2+ha3^2)
        
        pb:=sqrt(pb1^2+pb2^2)
        ps:=sqrt(ps1^2)
        pt:=sqrt(pt1^2+pt2^2)
        pa:=sqrt(pa1^2+pa2^2+pa3^2)
        
        fb:=sqrt(fb1^2+fb2^2+fb3^2)
        fs:=sqrt(fs1^2)
        ft:=sqrt(ft1^2+ft2^2+ft3^2)
        fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 25 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        29
##                                                       
##                                                   Used       Total
##   Number of observations                           191         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 6.635
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.468
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop_gr ~                                                             
##     chla_1   (hb1)     0.038    0.058    0.651    0.515    0.038    0.038
##     hzoop_1  (hs1)    -0.502    0.050  -10.119    0.000   -0.502   -0.619
##     pzoop_1  (ht1)     0.065    0.048    1.355    0.175    0.065    0.081
##     potam_1  (ht2)    -0.134    0.044   -3.026    0.002   -0.134   -0.180
##     estfs__1 (ht3)    -0.126    0.053   -2.393    0.017   -0.126   -0.150
##     flow     (ha1)    -0.040    0.058   -0.694    0.488   -0.040   -0.046
##     temp     (ha2)    -0.048    0.055   -0.866    0.386   -0.048   -0.054
##     turbid   (ha3)     0.047    0.061    0.759    0.448    0.047    0.050
##   pzoop_gr ~                                                             
##     hzoop_1  (pb1)     0.072    0.103    0.700    0.484    0.072    0.045
##     pzoop_1  (ps1)    -0.898    0.100   -9.017    0.000   -0.898   -0.568
##     potam_1  (pt1)    -0.180    0.092   -1.966    0.049   -0.180   -0.121
##     estfs__1 (pt2)     0.017    0.110    0.150    0.881    0.017    0.010
##     flow     (pa1)     0.118    0.120    0.984    0.325    0.118    0.068
##     temp     (pa2)    -0.015    0.115   -0.132    0.895   -0.015   -0.009
##     turbid   (pa3)     0.330    0.128    2.571    0.010    0.330    0.176
##   estfish_bsmt_gr ~                                                      
##     hzoop_1  (fb1)    -0.409    0.133   -3.073    0.002   -0.409   -0.187
##     pzoop_1  (fb2)     0.240    0.132    1.822    0.068    0.240    0.112
##     estfs__1 (fs1)    -1.365    0.151   -9.060    0.000   -1.365   -0.599
##     flow     (fa1)     0.176    0.161    1.093    0.274    0.176    0.075
##     temp     (fa2)    -0.027    0.153   -0.179    0.858   -0.027   -0.012
##     turbid   (fa3)     0.474    0.172    2.764    0.006    0.474    0.187
##     mrfsh__1 (ft1)    -0.033    0.150   -0.218    0.828   -0.033   -0.013
##     sbss1__1 (ft2)    -0.042    0.152   -0.278    0.781   -0.042   -0.018
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop_gr ~~                                                           
##    .pzoop_gr         -0.080    0.068   -1.170    0.242   -0.080   -0.085
##    .estfsh_bsmt_gr   -0.049    0.089   -0.548    0.584   -0.049   -0.040
##  .pzoop_gr ~~                                                           
##    .estfsh_bsmt_gr   -0.401    0.189   -2.120    0.034   -0.401   -0.155
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop_gr          0.451    0.046    9.772    0.000    0.451    0.625
##    .pzoop_gr          1.973    0.202    9.772    0.000    1.973    0.685
##    .estfsh_bsmt_gr    3.377    0.346    9.772    0.000    3.377    0.643
## 
## R-Square:
##                    Estimate
##     hzoop_gr          0.375
##     pzoop_gr          0.315
##     estfsh_bsmt_gr    0.357
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.038    0.058    0.651    0.515    0.038    0.038
##     hs                0.502    0.050   10.119    0.000    0.502    0.619
##     ht                0.196    0.044    4.454    0.000    0.196    0.248
##     ha                0.078    0.061    1.269    0.204    0.078    0.086
##     pb                0.072    0.103    0.700    0.484    0.072    0.045
##     ps                0.898    0.100    9.017    0.000    0.898    0.568
##     pt                0.181    0.093    1.938    0.053    0.181    0.121
##     pa                0.350    0.112    3.139    0.002    0.350    0.189
##     fb                0.475    0.138    3.431    0.001    0.475    0.218
##     fs                1.365    0.151    9.060    0.000    1.365    0.599
##     ft                0.053    0.156    0.343    0.732    0.053    0.022
##     fa                0.507    0.149    3.410    0.001    0.507    0.202
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 30 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        29
##                                                       
##                                                   Used       Total
##   Number of observations                           210         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 2.648
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.618
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop_gr ~                                                             
##     chla_1   (hb1)     0.050    0.036    1.393    0.164    0.050    0.088
##     hzoop_1  (hs1)    -0.299    0.039   -7.770    0.000   -0.299   -0.566
##     pzoop_1  (ht1)     0.024    0.035    0.689    0.491    0.024    0.045
##     potam_1  (ht2)    -0.120    0.037   -3.276    0.001   -0.120   -0.218
##     estfs__1 (ht3)    -0.010    0.034   -0.303    0.762   -0.010   -0.019
##     flow     (ha1)     0.076    0.038    2.007    0.045    0.076    0.138
##     temp     (ha2)     0.000    0.033    0.006    0.995    0.000    0.000
##     turbid   (ha3)    -0.110    0.037   -2.987    0.003   -0.110   -0.198
##   pzoop_gr ~                                                             
##     chla_1   (pb1)     0.176    0.054    3.278    0.001    0.176    0.209
##     hzoop_1  (pb2)     0.077    0.057    1.370    0.171    0.077    0.099
##     pzoop_1  (ps1)    -0.418    0.051   -8.229    0.000   -0.418   -0.527
##     potam_1  (pt1)    -0.070    0.055   -1.290    0.197   -0.070   -0.086
##     estfs__1 (pt2)     0.024    0.050    0.486    0.627    0.024    0.030
##     flow     (pa1)    -0.086    0.055   -1.561    0.119   -0.086   -0.106
##     temp     (pa2)     0.144    0.048    2.995    0.003    0.144    0.186
##     turbid   (pa3)     0.070    0.054    1.293    0.196    0.070    0.085
##   estfish_bsmt_gr ~                                                      
##     hzoop_1  (fb1)     0.134    0.105    1.274    0.203    0.134    0.082
##     pzoop_1  (fb2)    -0.120    0.102   -1.184    0.237   -0.120   -0.073
##     estfs__1 (fs1)    -1.007    0.107   -9.449    0.000   -1.007   -0.606
##     flow     (fa1)    -0.244    0.110   -2.226    0.026   -0.244   -0.144
##     temp     (fa2)     0.051    0.095    0.536    0.592    0.051    0.032
##     turbid   (fa3)     0.310    0.105    2.963    0.003    0.310    0.181
##     sbss1__1 (ft1)     0.262    0.103    2.536    0.011    0.262    0.156
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop_gr ~~                                                           
##    .pzoop_gr          0.076    0.022    3.524    0.000    0.076    0.251
##    .estfsh_bsmt_gr   -0.118    0.043   -2.766    0.006   -0.118   -0.194
##  .pzoop_gr ~~                                                           
##    .estfsh_bsmt_gr   -0.033    0.061   -0.544    0.586   -0.033   -0.038
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop_gr          0.207    0.020   10.247    0.000    0.207    0.743
##    .pzoop_gr          0.444    0.043   10.247    0.000    0.444    0.722
##    .estfsh_bsmt_gr    1.777    0.173   10.247    0.000    1.777    0.667
## 
## R-Square:
##                    Estimate
##     hzoop_gr          0.257
##     pzoop_gr          0.278
##     estfsh_bsmt_gr    0.333
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.050    0.036    1.393    0.164    0.050    0.088
##     hs                0.299    0.039    7.770    0.000    0.299    0.566
##     ht                0.123    0.037    3.338    0.001    0.123    0.224
##     ha                0.133    0.041    3.264    0.001    0.133    0.241
##     pb                0.193    0.048    4.009    0.000    0.193    0.231
##     ps                0.418    0.051    8.229    0.000    0.418    0.527
##     pt                0.074    0.052    1.424    0.154    0.074    0.091
##     pa                0.182    0.055    3.320    0.001    0.182    0.230
##     fb                0.180    0.119    1.509    0.131    0.180    0.110
##     fs                1.007    0.107    9.449    0.000    1.007    0.606
##     ft                0.262    0.103    2.536    0.011    0.262    0.156
##     fa                0.398    0.121    3.285    0.001    0.398    0.233
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 28 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        31
##                                                       
##                                                   Used       Total
##   Number of observations                           193         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 7.790
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.454
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop_gr ~                                                             
##     chla_1   (hb1)     0.037    0.054    0.694    0.488    0.037    0.040
##     hzoop_1  (hs1)    -0.536    0.053  -10.212    0.000   -0.536   -0.610
##     pzoop_1  (ht1)     0.003    0.054    0.052    0.959    0.003    0.003
##     corbic_1 (ht2)     0.028    0.050    0.547    0.584    0.028    0.031
##     estfs__1 (ht3)    -0.058    0.051   -1.137    0.256   -0.058   -0.071
##     flow     (ha1)     0.135    0.056    2.392    0.017    0.135    0.156
##     temp     (ha2)     0.035    0.049    0.703    0.482    0.035    0.041
##     turbid   (ha3)     0.148    0.052    2.852    0.004    0.148    0.167
##   pzoop_gr ~                                                             
##     chla_1   (pb1)     0.385    0.100    3.857    0.000    0.385    0.219
##     hzoop_1  (pb2)     0.196    0.097    2.022    0.043    0.196    0.119
##     pzoop_1  (ps1)    -1.033    0.100  -10.280    0.000   -1.033   -0.631
##     corbic_1 (pt1)     0.087    0.094    0.934    0.350    0.087    0.053
##     estfs__1 (pt2)    -0.092    0.093   -0.984    0.325   -0.092   -0.061
##     flow     (pa1)    -0.543    0.104   -5.229    0.000   -0.543   -0.337
##     temp     (pa2)     0.061    0.091    0.674    0.500    0.061    0.039
##     turbid   (pa3)     0.103    0.096    1.073    0.283    0.103    0.062
##   estfish_bsmt_gr ~                                                      
##     hzoop_1  (fb1)     0.324    0.178    1.825    0.068    0.324    0.122
##     pzoop_1  (fb2)     0.084    0.179    0.470    0.639    0.084    0.032
##     estfs__1 (fs1)    -1.431    0.171   -8.371    0.000   -1.431   -0.587
##     flow     (fa1)    -0.646    0.188   -3.444    0.001   -0.646   -0.247
##     temp     (fa2)     0.047    0.162    0.289    0.772    0.047    0.019
##     turbid   (fa3)     0.107    0.175    0.613    0.540    0.107    0.040
##     sside_1  (ft1)    -0.023    0.161   -0.145    0.884   -0.023   -0.009
##     cent_1   (ft2)    -0.270    0.165   -1.642    0.101   -0.270   -0.106
##     sbss1__1 (ft3)     0.193    0.170    1.135    0.257    0.193    0.076
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop_gr ~~                                                           
##    .pzoop_gr          0.263    0.062    4.229    0.000    0.263    0.320
##    .estfsh_bsmt_gr   -0.199    0.107   -1.872    0.061   -0.199   -0.136
##  .pzoop_gr ~~                                                           
##    .estfsh_bsmt_gr    0.076    0.195    0.389    0.697    0.076    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop_gr          0.447    0.045    9.823    0.000    0.447    0.610
##    .pzoop_gr          1.521    0.155    9.823    0.000    1.521    0.595
##    .estfsh_bsmt_gr    4.814    0.490    9.823    0.000    4.814    0.717
## 
## R-Square:
##                    Estimate
##     hzoop_gr          0.390
##     pzoop_gr          0.405
##     estfsh_bsmt_gr    0.283
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.037    0.054    0.694    0.488    0.037    0.040
##     hs                0.536    0.053   10.212    0.000    0.536    0.610
##     ht                0.064    0.053    1.207    0.228    0.064    0.078
##     ha                0.203    0.051    4.008    0.000    0.203    0.232
##     pb                0.432    0.097    4.469    0.000    0.432    0.250
##     ps                1.033    0.100   10.280    0.000    1.033    0.631
##     pt                0.127    0.098    1.296    0.195    0.127    0.081
##     pa                0.556    0.104    5.345    0.000    0.556    0.344
##     fb                0.335    0.166    2.017    0.044    0.335    0.126
##     fs                1.431    0.171    8.371    0.000    1.431    0.587
##     ft                0.333    0.151    2.201    0.028    0.333    0.130
##     fa                0.657    0.189    3.469    0.001    0.657    0.251
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 32 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        32
##                                                       
##                                                   Used       Total
##   Number of observations                           199         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 3.561
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.829
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   hzoop_gr ~                                                             
##     chla_1   (hb1)     0.140    0.046    3.010    0.003    0.140    0.186
##     hzoop_1  (hs1)    -0.403    0.044   -9.077    0.000   -0.403   -0.553
##     pzoop_1  (ht1)    -0.028    0.049   -0.570    0.568   -0.028   -0.037
##     corbic_1 (ht2)     0.030    0.045    0.678    0.498    0.030    0.040
##     estfs__1 (ht3)     0.007    0.044    0.148    0.882    0.007    0.009
##     flow     (ha1)     0.104    0.046    2.256    0.024    0.104    0.136
##     temp     (ha2)     0.049    0.043    1.155    0.248    0.049    0.069
##     turbid   (ha3)    -0.043    0.043   -0.986    0.324   -0.043   -0.060
##   pzoop_gr ~                                                             
##     chla_1   (pb1)     0.336    0.077    4.387    0.000    0.336    0.265
##     hzoop_1  (pb2)     0.130    0.073    1.766    0.077    0.130    0.105
##     pzoop_1  (ps1)    -0.772    0.080   -9.614    0.000   -0.772   -0.603
##     corbic_1 (pt1)    -0.019    0.073   -0.257    0.797   -0.019   -0.015
##     estfs__1 (pt2)     0.036    0.073    0.490    0.624    0.036    0.030
##     flow     (pa1)    -0.140    0.076   -1.841    0.066   -0.140   -0.108
##     temp     (pa2)    -0.043    0.070   -0.612    0.540   -0.043   -0.036
##     turbid   (pa3)     0.107    0.072    1.495    0.135    0.107    0.089
##   estfish_bsmt_gr ~                                                      
##     chla_1   (fb1)     0.226    0.183    1.235    0.217    0.226    0.076
##     hzoop_1  (fb2)     0.341    0.178    1.919    0.055    0.341    0.118
##     pzoop_1  (fb3)    -0.009    0.192   -0.048    0.962   -0.009   -0.003
##     estfs__1 (fs1)    -1.574    0.178   -8.863    0.000   -1.574   -0.561
##     flow     (fa1)    -0.152    0.184   -0.827    0.409   -0.152   -0.050
##     temp     (fa2)    -0.054    0.168   -0.323    0.746   -0.054   -0.019
##     turbid   (fa3)     0.468    0.186    2.521    0.012    0.468    0.164
##     sside_1  (ft1)    -0.026    0.166   -0.159    0.874   -0.026   -0.009
##     cent_1   (ft2)    -0.237    0.173   -1.372    0.170   -0.237   -0.086
##     sbss1__1 (ft3)     0.138    0.176    0.783    0.434    0.138    0.049
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .hzoop_gr ~~                                                           
##    .pzoop_gr          0.180    0.043    4.240    0.000    0.180    0.315
##    .estfsh_bsmt_gr   -0.162    0.097   -1.671    0.095   -0.162   -0.119
##  .pzoop_gr ~~                                                           
##    .estfsh_bsmt_gr    0.381    0.161    2.358    0.018    0.381    0.170
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .hzoop_gr          0.346    0.035    9.975    0.000    0.346    0.685
##    .pzoop_gr          0.946    0.095    9.975    0.000    0.946    0.656
##    .estfsh_bsmt_gr    5.325    0.534    9.975    0.000    5.325    0.671
## 
## R-Square:
##                    Estimate
##     hzoop_gr          0.315
##     pzoop_gr          0.344
##     estfsh_bsmt_gr    0.329
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     hb                0.140    0.046    3.010    0.003    0.140    0.186
##     hs                0.403    0.044    9.077    0.000    0.403    0.553
##     ht                0.042    0.048    0.856    0.392    0.042    0.055
##     ha                0.122    0.047    2.629    0.009    0.122    0.164
##     pb                0.360    0.073    4.928    0.000    0.360    0.285
##     ps                0.772    0.080    9.614    0.000    0.772    0.603
##     pt                0.040    0.072    0.563    0.573    0.040    0.033
##     pa                0.181    0.078    2.332    0.020    0.181    0.144
##     fb                0.409    0.169    2.421    0.015    0.409    0.141
##     fs                1.574    0.178    8.863    0.000    1.574    0.561
##     ft                0.276    0.184    1.501    0.133    0.276    0.099
##     fa                0.495    0.195    2.533    0.011    0.495    0.173
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitS)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
upper_plot_far_west <- createGraph(fit=modfitFW, 
                                   reference_df=cnameslag, 
                                   model_type="monthly_upper_trophic",
                                   title="Far West",
                                   manual_port_settings=TRUE)
upper_plot_far_west
#WEST
upper_plot_west <- createGraph(fit=modfitW, 
                               reference_df=cnameslag, 
                               model_type="monthly_upper_trophic",
                               title="West",
                               manual_port_settings=TRUE)
upper_plot_west
#NORTH
upper_plot_north <- createGraph(fit=modfitN, 
                                reference_df=cnameslag, 
                                model_type="monthly_upper_trophic",
                                title="North",
                                manual_port_settings=TRUE)
upper_plot_north
#SOUTH
upper_plot_south <- createGraph(fit=modfitS, 
                                reference_df=cnameslag, 
                                model_type="monthly_upper_trophic",
                                title="South",
                                manual_port_settings=TRUE)
upper_plot_south

Lower trophic

modFW='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
        chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
        potam~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modW='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
        chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
        potam~lb1*din_1+lb2*chla_1+lb3*hzoop_1+lb4*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2+lb4^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modN='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
        chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ct3*pzoop_1+ca1*flow+ca2*temp+ca3*turbid
        corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2+ct3^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modS='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
        chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ca1*flow+ca2*temp+ca3*turbid
        corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
        
        ns:=sqrt(ns1^2)
        nt:=sqrt(nt1^2)
        nn:=sqrt(nn1^2+nn2^2+nn3^2)
        na:=sqrt(na1^2+na2^2+na3^2)
        
        cb:=sqrt(cb1^2)
        cs:=sqrt(cs1^2)
        ct:=sqrt(ct1^2+ct2^2)
        ca:=sqrt(ca1^2+ca2^2+ca3^2)
        
        lb:=sqrt(lb1^2+lb2^2+lb3^2)
        ls:=sqrt(ls1^2)
        la:=sqrt(la1^2+la2^2+la3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           234         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 5.222
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.265
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din_gr ~                                                              
##     din_1    (ns1)   -0.218    0.025   -8.695    0.000   -0.218   -0.487
##     chla_gr  (nt1)   -0.160    0.051   -3.135    0.002   -0.160   -0.175
##     hzoop_1  (nn1)   -0.001    0.026   -0.033    0.974   -0.001   -0.002
##     pzoop_1  (nn2)   -0.010    0.025   -0.400    0.689   -0.010   -0.023
##     potam_1  (nn3)   -0.002    0.025   -0.068    0.946   -0.002   -0.004
##     flow     (na1)    0.003    0.029    0.113    0.910    0.003    0.007
##     temp     (na2)   -0.046    0.028   -1.641    0.101   -0.046   -0.098
##     turbid   (na3)   -0.054    0.029   -1.839    0.066   -0.054   -0.113
##   chla_gr ~                                                             
##     din_1    (cb1)    0.000    0.027    0.004    0.997    0.000    0.000
##     chla_1   (cs1)   -0.323    0.034   -9.629    0.000   -0.323   -0.538
##     hzoop_1  (ct1)   -0.002    0.028   -0.084    0.933   -0.002   -0.005
##     potam_1  (ct2)   -0.015    0.027   -0.548    0.584   -0.015   -0.031
##     flow     (ca1)    0.014    0.031    0.463    0.644    0.014    0.028
##     temp     (ca2)    0.044    0.030    1.470    0.141    0.044    0.086
##     turbid   (ca3)   -0.025    0.032   -0.788    0.431   -0.025   -0.048
##   potam ~                                                               
##     chla_1   (lb1)    0.056    0.060    0.939    0.348    0.056    0.045
##     hzoop_1  (lb2)   -0.074    0.051   -1.437    0.151   -0.074   -0.071
##     pzoop_1  (lb3)   -0.163    0.050   -3.266    0.001   -0.163   -0.161
##     potam_1  (ls1)    0.630    0.048   13.022    0.000    0.630    0.630
##     flow     (la1)    0.113    0.057    1.981    0.048    0.113    0.103
##     temp     (la2)   -0.043    0.054   -0.799    0.425   -0.043   -0.040
##     turbid   (la3)    0.036    0.058    0.624    0.533    0.036    0.033
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din_gr ~~                                                             
##    .potam            -0.017    0.019   -0.916    0.360   -0.017   -0.060
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din_gr            0.147    0.014   10.817    0.000    0.147    0.708
##    .chla_gr           0.172    0.016   10.817    0.000    0.172    0.694
##    .potam             0.568    0.053   10.817    0.000    0.568    0.518
## 
## R-Square:
##                    Estimate
##     din_gr            0.292
##     chla_gr           0.306
##     potam             0.482
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.218    0.025    8.695    0.000    0.218    0.487
##     nt                0.160    0.051    3.135    0.002    0.160    0.175
##     nn                0.010    0.025    0.407    0.684    0.010    0.023
##     na                0.071    0.032    2.207    0.027    0.071    0.150
##     cb                0.000    0.027    0.004    0.997    0.000    0.000
##     cs                0.323    0.034    9.629    0.000    0.323    0.538
##     ct                0.015    0.028    0.536    0.592    0.015    0.031
##     ca                0.053    0.029    1.791    0.073    0.053    0.103
##     lb                0.188    0.049    3.824    0.000    0.188    0.182
##     ls                0.630    0.048   13.022    0.000    0.630    0.630
##     la                0.127    0.049    2.578    0.010    0.127    0.116
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 25 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        27
##                                                       
##                                                   Used       Total
##   Number of observations                           257         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                17.150
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.001
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din_gr ~                                                              
##     din_1    (ns1)   -0.196    0.020   -9.655    0.000   -0.196   -0.567
##     chla_gr  (nt1)   -0.122    0.033   -3.700    0.000   -0.122   -0.191
##     hzoop_1  (nn1)   -0.013    0.020   -0.647    0.518   -0.013   -0.039
##     pzoop_1  (nn2)    0.010    0.019    0.504    0.614    0.010    0.028
##     potam_1  (nn3)    0.020    0.021    0.969    0.333    0.020    0.059
##     flow     (na1)   -0.121    0.021   -5.654    0.000   -0.121   -0.341
##     temp     (na2)    0.020    0.018    1.072    0.284    0.020    0.057
##     turbid   (na3)    0.039    0.020    1.979    0.048    0.039    0.113
##   chla_gr ~                                                             
##     din_1    (cb1)   -0.063    0.032   -1.958    0.050   -0.063   -0.117
##     chla_1   (cs1)   -0.370    0.034  -11.003    0.000   -0.370   -0.618
##     hzoop_1  (ct1)    0.048    0.031    1.556    0.120    0.048    0.091
##     potam_1  (ct2)    0.014    0.033    0.425    0.671    0.014    0.026
##     flow     (ca1)    0.001    0.033    0.026    0.980    0.001    0.002
##     temp     (ca2)   -0.042    0.028   -1.487    0.137   -0.042   -0.079
##     turbid   (ca3)   -0.012    0.031   -0.398    0.691   -0.012   -0.023
##   potam ~                                                               
##     din_1    (lb1)    0.076    0.044    1.731    0.083    0.076    0.075
##     chla_1   (lb2)   -0.006    0.046   -0.124    0.901   -0.006   -0.005
##     hzoop_1  (lb3)   -0.008    0.044   -0.172    0.864   -0.008   -0.008
##     pzoop_1  (lb4)    0.070    0.041    1.738    0.082    0.070    0.070
##     potam_1  (ls1)    0.707    0.044   15.987    0.000    0.707    0.701
##     flow     (la1)   -0.112    0.045   -2.496    0.013   -0.112   -0.108
##     temp     (la2)   -0.008    0.039   -0.201    0.841   -0.008   -0.008
##     turbid   (la3)   -0.079    0.042   -1.884    0.060   -0.079   -0.078
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din_gr ~~                                                             
##    .potam             0.013    0.011    1.209    0.227    0.013    0.076
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din_gr            0.081    0.007   11.336    0.000    0.081    0.680
##    .chla_gr           0.195    0.017   11.336    0.000    0.195    0.674
##    .potam             0.359    0.032   11.336    0.000    0.359    0.351
## 
## R-Square:
##                    Estimate
##     din_gr            0.320
##     chla_gr           0.326
##     potam             0.649
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.196    0.020    9.655    0.000    0.196    0.567
##     nt                0.122    0.033    3.700    0.000    0.122    0.191
##     nn                0.026    0.021    1.265    0.206    0.026    0.076
##     na                0.128    0.022    5.778    0.000    0.128    0.364
##     cb                0.063    0.032    1.958    0.050    0.063    0.117
##     cs                0.370    0.034   11.003    0.000    0.370    0.618
##     ct                0.050    0.033    1.512    0.131    0.050    0.094
##     ca                0.044    0.030    1.464    0.143    0.044    0.082
##     lb                0.104    0.042    2.490    0.013    0.104    0.103
##     ls                0.707    0.044   15.987    0.000    0.707    0.701
##     la                0.137    0.040    3.416    0.001    0.137    0.133
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        27
##                                                       
##                                                   Used       Total
##   Number of observations                           255         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                23.323
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din_gr ~                                                              
##     din_1    (ns1)   -0.336    0.026  -13.181    0.000   -0.336   -0.679
##     chla_gr  (nt1)   -0.108    0.033   -3.258    0.001   -0.108   -0.155
##     hzoop_1  (nn1)    0.011    0.025    0.461    0.645    0.011    0.023
##     pzoop_1  (nn2)    0.057    0.026    2.209    0.027    0.057    0.113
##     corbic_1 (nn3)   -0.010    0.024   -0.408    0.684   -0.010   -0.019
##     flow     (na1)   -0.175    0.027   -6.459    0.000   -0.175   -0.344
##     temp     (na2)    0.020    0.024    0.839    0.402    0.020    0.041
##     turbid   (na3)    0.016    0.023    0.697    0.486    0.016    0.033
##   chla_gr ~                                                             
##     din_1    (cb1)    0.012    0.040    0.309    0.758    0.012    0.017
##     chla_1   (cs1)   -0.398    0.037  -10.807    0.000   -0.398   -0.561
##     hzoop_1  (ct1)   -0.015    0.038   -0.393    0.695   -0.015   -0.021
##     corbic_1 (ct2)    0.013    0.038    0.350    0.726    0.013    0.018
##     pzoop_1  (ct3)   -0.087    0.040   -2.155    0.031   -0.087   -0.119
##     flow     (ca1)   -0.048    0.042   -1.138    0.255   -0.048   -0.066
##     temp     (ca2)    0.058    0.038    1.557    0.119    0.058    0.082
##     turbid   (ca3)    0.046    0.036    1.268    0.205    0.046    0.065
##   corbic ~                                                              
##     chla_1   (lb1)   -0.001    0.053   -0.021    0.983   -0.001   -0.001
##     hzoop_1  (lb2)    0.011    0.056    0.194    0.847    0.011    0.011
##     pzoop_1  (lb3)   -0.010    0.058   -0.177    0.859   -0.010   -0.010
##     corbic_1 (ls1)    0.462    0.056    8.308    0.000    0.462    0.460
##     flow     (la1)    0.093    0.060    1.553    0.120    0.093    0.092
##     temp     (la2)   -0.034    0.055   -0.616    0.538   -0.034   -0.035
##     turbid   (la3)   -0.117    0.053   -2.207    0.027   -0.117   -0.121
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din_gr ~~                                                             
##    .corbic           -0.010    0.019   -0.504    0.614   -0.010   -0.032
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din_gr            0.135    0.012   11.292    0.000    0.135    0.547
##    .chla_gr           0.331    0.029   11.292    0.000    0.331    0.647
##    .corbic            0.715    0.063   11.292    0.000    0.715    0.740
## 
## R-Square:
##                    Estimate
##     din_gr            0.453
##     chla_gr           0.353
##     corbic            0.260
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.336    0.026   13.181    0.000    0.336    0.679
##     nt                0.108    0.033    3.258    0.001    0.108    0.155
##     nn                0.059    0.025    2.413    0.016    0.059    0.117
##     na                0.177    0.027    6.577    0.000    0.177    0.348
##     cb                0.012    0.040    0.309    0.758    0.012    0.017
##     cs                0.398    0.037   10.807    0.000    0.398    0.561
##     ct                0.089    0.038    2.326    0.020    0.089    0.122
##     ca                0.089    0.038    2.325    0.020    0.089    0.124
##     lb                0.015    0.063    0.241    0.810    0.015    0.015
##     ls                0.462    0.056    8.308    0.000    0.462    0.460
##     la                0.153    0.057    2.686    0.007    0.153    0.156
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           256         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 2.541
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.637
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   din_gr ~                                                              
##     din_1    (ns1)   -0.297    0.028  -10.569    0.000   -0.297   -0.576
##     chla_gr  (nt1)   -0.100    0.035   -2.895    0.004   -0.100   -0.149
##     hzoop_1  (nn1)   -0.015    0.029   -0.510    0.610   -0.015   -0.027
##     pzoop_1  (nn2)   -0.010    0.030   -0.341    0.733   -0.010   -0.018
##     corbic_1 (nn3)    0.038    0.027    1.379    0.168    0.038    0.073
##     flow     (na1)   -0.007    0.028   -0.262    0.794   -0.007   -0.014
##     temp     (na2)    0.045    0.027    1.660    0.097    0.045    0.086
##     turbid   (na3)    0.091    0.029    3.169    0.002    0.091    0.175
##   chla_gr ~                                                             
##     din_1    (cb1)   -0.017    0.042   -0.400    0.689   -0.017   -0.022
##     chla_1   (cs1)   -0.441    0.042  -10.576    0.000   -0.441   -0.567
##     hzoop_1  (ct1)    0.060    0.044    1.359    0.174    0.060    0.075
##     corbic_1 (ct2)    0.048    0.041    1.170    0.242    0.048    0.063
##     flow     (ca1)   -0.076    0.042   -1.822    0.068   -0.076   -0.097
##     temp     (ca2)    0.008    0.041    0.186    0.853    0.008    0.010
##     turbid   (ca3)    0.015    0.044    0.336    0.737    0.015    0.019
##   corbic ~                                                              
##     chla_1   (lb1)    0.046    0.059    0.775    0.438    0.046    0.046
##     hzoop_1  (lb2)   -0.023    0.062   -0.370    0.712   -0.023   -0.022
##     pzoop_1  (lb3)   -0.040    0.063   -0.637    0.524   -0.040   -0.038
##     corbic_1 (ls1)    0.315    0.058    5.474    0.000    0.315    0.318
##     flow     (la1)    0.081    0.059    1.384    0.166    0.081    0.080
##     temp     (la2)   -0.078    0.058   -1.349    0.177   -0.078   -0.077
##     turbid   (la3)    0.185    0.058    3.205    0.001    0.185    0.186
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .din_gr ~~                                                             
##    .corbic           -0.024    0.024   -0.980    0.327   -0.024   -0.061
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .din_gr            0.182    0.016   11.314    0.000    0.182    0.662
##    .chla_gr           0.415    0.037   11.314    0.000    0.415    0.688
##    .corbic            0.812    0.072   11.314    0.000    0.812    0.809
## 
## R-Square:
##                    Estimate
##     din_gr            0.338
##     chla_gr           0.312
##     corbic            0.191
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ns                0.297    0.028   10.569    0.000    0.297    0.576
##     nt                0.100    0.035    2.895    0.004    0.100    0.149
##     nn                0.042    0.028    1.490    0.136    0.042    0.080
##     na                0.102    0.029    3.487    0.000    0.102    0.196
##     cb                0.017    0.042    0.400    0.689    0.017    0.022
##     cs                0.441    0.042   10.576    0.000    0.441    0.567
##     ct                0.077    0.041    1.872    0.061    0.077    0.098
##     ca                0.078    0.043    1.831    0.067    0.078    0.099
##     lb                0.065    0.066    0.987    0.324    0.065    0.063
##     ls                0.315    0.058    5.474    0.000    0.315    0.318
##     la                0.217    0.055    3.918    0.000    0.217    0.217
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
lower_plot_far_west <- createGraph(fit=modfitFW, 
                                   reference_df=cnameslag, 
                                   model_type="monthly_lower_trophic",
                                   title="Far West",
                                   manual_port_settings=TRUE)
lower_plot_far_west
#WEST
lower_plot_west <- createGraph(fit=modfitW, 
                               reference_df=cnameslag, 
                               model_type="monthly_lower_trophic",
                               title="West",
                               manual_port_settings=TRUE)
lower_plot_west
#NORTH
lower_plot_north <- createGraph(fit=modfitN, 
                                reference_df=cnameslag, 
                                model_type="monthly_lower_trophic",
                                title="North",
                                manual_port_settings=TRUE)
lower_plot_north
#SOUTH
lower_plot_south <- createGraph(fit=modfitS, 
                                reference_df=cnameslag, 
                                model_type="monthly_lower_trophic",
                                title="South",
                                manual_port_settings=TRUE)
lower_plot_south

Zooplankton

modFW='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp
       hcope_gr~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope_gr~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
       estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+flow+turbid+temp
'
modW='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp+mysid_1
       hcope_gr~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
       amphi_m_gr~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope_gr~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
       mysid_gr~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
       estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+mysid_1+flow+turbid+temp
'
modN='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+corbic_1+flow+turbid+temp
       hcope_gr~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope_gr~hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1+chla_1
       mysid_gr~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+mysid_1+flow+turbid+temp
'
modS='chla_gr~chla_1+hcope_1+clad_1+rotif_m_1+corbic_1+flow+turbid+temp
       hcope_gr~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
       clad_gr~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
       amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
       rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
       pcope_gr~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
       estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+clad_1+flow+turbid+temp
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 46 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        64
##                                                       
##                                                   Used       Total
##   Number of observations                           183         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                22.686
##   Degrees of freedom                                17
##   P-value (Chi-square)                           0.160
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla_gr ~                                                              
##     chla_1            -0.339    0.037   -9.191    0.000   -0.339   -0.565
##     hcope_1            0.046    0.030    1.539    0.124    0.046    0.095
##     amphi_m_1          0.012    0.036    0.317    0.751    0.012    0.024
##     rotif_m_1         -0.029    0.035   -0.842    0.400   -0.029   -0.055
##     potam_1           -0.006    0.027   -0.209    0.834   -0.006   -0.013
##     flow               0.032    0.040    0.807    0.420    0.032    0.062
##     turbid            -0.037    0.039   -0.944    0.345   -0.037   -0.066
##     temp               0.028    0.034    0.817    0.414    0.028    0.053
##   hcope_gr ~                                                             
##     chla_1             0.147    0.080    1.843    0.065    0.147    0.104
##     hcope_1           -0.720    0.070  -10.354    0.000   -0.720   -0.627
##     pcope_1            0.068    0.072    0.943    0.346    0.068    0.056
##     potam_1           -0.112    0.061   -1.815    0.069   -0.112   -0.107
##     flow              -0.075    0.081   -0.920    0.357   -0.075   -0.061
##     turbid            -0.048    0.085   -0.559    0.576   -0.048   -0.036
##     temp              -0.047    0.077   -0.617    0.537   -0.047   -0.038
##     estfish_bsmt_1    -0.137    0.077   -1.780    0.075   -0.137   -0.114
##   amphi_m_gr ~                                                           
##     chla_1             0.071    0.083    0.857    0.391    0.071    0.056
##     amphi_m_1         -0.533    0.080   -6.701    0.000   -0.533   -0.521
##     flow              -0.510    0.089   -5.747    0.000   -0.510   -0.467
##     turbid             0.003    0.086    0.040    0.968    0.003    0.003
##     temp               0.063    0.077    0.821    0.412    0.063    0.056
##     estfish_bsmt_1     0.096    0.074    1.292    0.196    0.096    0.090
##   rotif_m_gr ~                                                           
##     chla_1            -0.394    0.163   -2.419    0.016   -0.394   -0.148
##     rotif_m_1         -1.313    0.147   -8.954    0.000   -1.313   -0.549
##     flow               0.148    0.161    0.919    0.358    0.148    0.065
##     turbid             0.083    0.170    0.486    0.627    0.083    0.034
##     temp               0.071    0.152    0.465    0.642    0.071    0.030
##     estfish_bsmt_1    -0.007    0.143   -0.052    0.958   -0.007   -0.003
##   pcope_gr ~                                                             
##     hcope_1            0.132    0.134    0.985    0.325    0.132    0.062
##     pcope_1           -1.295    0.138   -9.365    0.000   -1.295   -0.582
##     potam_1           -0.197    0.117   -1.682    0.092   -0.197   -0.102
##     flow               0.358    0.156    2.304    0.021    0.358    0.157
##     turbid             0.206    0.164    1.256    0.209    0.206    0.084
##     temp               0.241    0.148    1.623    0.104    0.241    0.103
##     estfish_bsmt_1    -0.030    0.147   -0.205    0.838   -0.030   -0.014
##   estfish_bsmt_gr ~                                                      
##     estfish_bsmt_1    -1.318    0.150   -8.780    0.000   -1.318   -0.587
##     hcope_1           -0.234    0.136   -1.725    0.084   -0.234   -0.109
##     pcope_1            0.314    0.141    2.224    0.026    0.314    0.140
##     amphi_m_1         -0.138    0.160   -0.862    0.389   -0.138   -0.064
##     rotif_m_1         -0.321    0.150   -2.139    0.032   -0.321   -0.134
##     flow               0.155    0.172    0.898    0.369    0.155    0.067
##     turbid             0.448    0.169    2.653    0.008    0.448    0.182
##     temp              -0.078    0.151   -0.515    0.607   -0.078   -0.033
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla_gr ~~                                                            
##    .hcope_gr          0.020    0.029    0.706    0.480    0.020    0.052
##    .amphi_m_gr       -0.001    0.029   -0.038    0.970   -0.001   -0.003
##    .rotif_m_gr        0.143    0.059    2.437    0.015    0.143    0.183
##    .pcope_gr          0.045    0.056    0.801    0.423    0.045    0.059
##    .estfsh_bsmt_gr   -0.055    0.057   -0.966    0.334   -0.055   -0.072
##  .hcope_gr ~~                                                           
##    .amphi_m_gr       -0.033    0.065   -0.506    0.613   -0.033   -0.037
##    .rotif_m_gr        0.149    0.129    1.159    0.246    0.149    0.086
##    .pcope_gr         -0.371    0.127   -2.913    0.004   -0.371   -0.220
##    .estfsh_bsmt_gr   -0.108    0.126   -0.856    0.392   -0.108   -0.063
##  .amphi_m_gr ~~                                                         
##    .rotif_m_gr       -0.234    0.130   -1.801    0.072   -0.234   -0.134
##    .pcope_gr          0.018    0.125    0.144    0.886    0.018    0.011
##    .estfsh_bsmt_gr    0.005    0.126    0.038    0.970    0.005    0.003
##  .rotif_m_gr ~~                                                         
##    .pcope_gr         -0.196    0.248   -0.791    0.429   -0.196   -0.059
##    .estfsh_bsmt_gr   -0.116    0.250   -0.466    0.641   -0.116   -0.034
##  .pcope_gr ~~                                                           
##    .estfsh_bsmt_gr   -0.513    0.245   -2.092    0.036   -0.513   -0.157
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla_gr           0.176    0.018    9.566    0.000    0.176    0.677
##    .hcope_gr          0.873    0.091    9.566    0.000    0.873    0.596
##    .amphi_m_gr        0.875    0.092    9.566    0.000    0.875    0.754
##    .rotif_m_gr        3.452    0.361    9.566    0.000    3.452    0.677
##    .pcope_gr          3.242    0.339    9.566    0.000    3.242    0.643
##    .estfsh_bsmt_gr    3.312    0.346    9.566    0.000    3.312    0.644
## 
## R-Square:
##                    Estimate
##     chla_gr           0.323
##     hcope_gr          0.404
##     amphi_m_gr        0.246
##     rotif_m_gr        0.323
##     pcope_gr          0.357
##     estfsh_bsmt_gr    0.356
summary(modfitW, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        87
##                                                       
##                                                   Used       Total
##   Number of observations                           202         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                24.900
##   Degrees of freedom                                18
##   P-value (Chi-square)                           0.128
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla_gr ~                                                              
##     chla_1            -0.352    0.037   -9.401    0.000   -0.352   -0.601
##     hcope_1            0.060    0.036    1.652    0.099    0.060    0.105
##     amphi_m_1          0.050    0.033    1.486    0.137    0.050    0.093
##     rotif_m_1          0.063    0.032    1.956    0.050    0.063    0.123
##     potam_1           -0.012    0.036   -0.328    0.743   -0.012   -0.021
##     flow              -0.009    0.037   -0.241    0.810   -0.009   -0.016
##     turbid             0.019    0.038    0.499    0.617    0.019    0.034
##     temp              -0.062    0.033   -1.910    0.056   -0.062   -0.117
##     mysid_1           -0.052    0.036   -1.449    0.147   -0.052   -0.093
##   hcope_gr ~                                                             
##     chla_1             0.043    0.040    1.100    0.272    0.043    0.067
##     hcope_1           -0.353    0.040   -8.853    0.000   -0.353   -0.561
##     pcope_1           -0.051    0.035   -1.455    0.146   -0.051   -0.087
##     mysid_1            0.034    0.041    0.841    0.400    0.034    0.055
##     potam_1           -0.088    0.037   -2.378    0.017   -0.088   -0.143
##     flow              -0.086    0.041   -2.110    0.035   -0.086   -0.139
##     turbid            -0.044    0.041   -1.064    0.287   -0.044   -0.071
##     temp               0.035    0.036    0.987    0.324    0.035    0.060
##     estfish_bsmt_1     0.016    0.036    0.461    0.645    0.016    0.027
##     rotif_m_1          0.101    0.034    2.995    0.003    0.101    0.179
##   amphi_m_gr ~                                                           
##     chla_1            -0.082    0.064   -1.282    0.200   -0.082   -0.086
##     amphi_m_1         -0.303    0.061   -4.933    0.000   -0.303   -0.346
##     mysid_1            0.154    0.062    2.486    0.013    0.154    0.168
##     flow               0.004    0.063    0.062    0.951    0.004    0.004
##     turbid            -0.210    0.066   -3.181    0.001   -0.210   -0.228
##     temp              -0.163    0.058   -2.819    0.005   -0.163   -0.187
##     estfish_bsmt_1    -0.251    0.061   -4.084    0.000   -0.251   -0.281
##   rotif_m_gr ~                                                           
##     chla_1             0.021    0.109    0.196    0.844    0.021    0.012
##     rotif_m_1         -0.886    0.096   -9.190    0.000   -0.886   -0.574
##     flow               0.307    0.110    2.792    0.005    0.307    0.182
##     turbid            -0.245    0.109   -2.249    0.025   -0.245   -0.144
##     temp              -0.003    0.098   -0.032    0.974   -0.003   -0.002
##     estfish_bsmt_1    -0.214    0.097   -2.202    0.028   -0.214   -0.130
##   pcope_gr ~                                                             
##     hcope_1           -0.136    0.061   -2.224    0.026   -0.136   -0.142
##     pcope_1           -0.495    0.057   -8.658    0.000   -0.495   -0.554
##     mysid_1            0.123    0.064    1.935    0.053    0.123    0.131
##     potam_1            0.032    0.063    0.509    0.611    0.032    0.034
##     flow               0.116    0.063    1.844    0.065    0.116    0.124
##     turbid            -0.073    0.065   -1.131    0.258   -0.073   -0.077
##     temp               0.184    0.055    3.360    0.001    0.184    0.206
##     estfish_bsmt_1     0.001    0.057    0.011    0.991    0.001    0.001
##     rotif_m_1          0.141    0.055    2.561    0.010    0.141    0.164
##   mysid_gr ~                                                             
##     chla_1             0.272    0.084    3.243    0.001    0.272    0.208
##     hcope_1            0.078    0.082    0.943    0.346    0.078    0.061
##     pcope_1            0.132    0.073    1.803    0.071    0.132    0.111
##     amphi_m_1         -0.120    0.076   -1.583    0.113   -0.120   -0.100
##     mysid_1           -0.699    0.086   -8.152    0.000   -0.699   -0.558
##     flow              -0.209    0.081   -2.577    0.010   -0.209   -0.167
##     turbid             0.329    0.087    3.776    0.000    0.329    0.261
##     temp               0.118    0.075    1.567    0.117    0.118    0.099
##     estfish_bsmt_1     0.012    0.078    0.153    0.878    0.012    0.010
##   estfish_bsmt_gr ~                                                      
##     estfish_bsmt_1    -0.970    0.104   -9.366    0.000   -0.970   -0.585
##     hcope_1            0.104    0.106    0.980    0.327    0.104    0.060
##     pcope_1           -0.028    0.101   -0.272    0.786   -0.028   -0.017
##     amphi_m_1         -0.270    0.104   -2.609    0.009   -0.270   -0.166
##     rotif_m_1          0.011    0.098    0.115    0.908    0.011    0.007
##     mysid_1           -0.163    0.112   -1.452    0.147   -0.163   -0.096
##     flow              -0.225    0.108   -2.080    0.038   -0.225   -0.133
##     turbid             0.259    0.114    2.266    0.023    0.259    0.152
##     temp               0.072    0.097    0.748    0.455    0.072    0.045
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla_gr ~~                                                            
##    .hcope_gr          0.058    0.016    3.617    0.000    0.058    0.263
##    .amphi_m_gr        0.006    0.025    0.243    0.808    0.006    0.017
##    .rotif_m_gr        0.126    0.044    2.858    0.004    0.126    0.205
##    .pcope_gr         -0.012    0.024   -0.493    0.622   -0.012   -0.035
##    .mysid_gr          0.080    0.033    2.438    0.015    0.080    0.174
##    .estfsh_bsmt_gr   -0.029    0.043   -0.675    0.499   -0.029   -0.048
##  .hcope_gr ~~                                                           
##    .amphi_m_gr       -0.046    0.028   -1.664    0.096   -0.046   -0.118
##    .rotif_m_gr       -0.027    0.047   -0.566    0.572   -0.027   -0.040
##    .pcope_gr          0.027    0.026    1.025    0.306    0.027    0.072
##    .mysid_gr          0.183    0.038    4.865    0.000    0.183    0.364
##    .estfsh_bsmt_gr   -0.121    0.047   -2.570    0.010   -0.121   -0.184
##  .amphi_m_gr ~~                                                         
##    .rotif_m_gr        0.127    0.077    1.656    0.098    0.127    0.117
##    .pcope_gr          0.010    0.043    0.245    0.806    0.010    0.017
##    .mysid_gr         -0.111    0.058   -1.908    0.056   -0.111   -0.135
##    .estfsh_bsmt_gr    0.044    0.075    0.585    0.558    0.044    0.041
##  .rotif_m_gr ~~                                                         
##    .pcope_gr          0.077    0.073    1.052    0.293    0.077    0.074
##    .mysid_gr         -0.102    0.099   -1.032    0.302   -0.102   -0.073
##    .estfsh_bsmt_gr    0.173    0.130    1.339    0.180    0.173    0.095
##  .pcope_gr ~~                                                           
##    .mysid_gr         -0.035    0.055   -0.634    0.526   -0.035   -0.045
##    .estfsh_bsmt_gr   -0.016    0.072   -0.217    0.828   -0.016   -0.015
##  .mysid_gr ~~                                                           
##    .estfsh_bsmt_gr   -0.069    0.097   -0.714    0.475   -0.069   -0.050
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla_gr           0.202    0.020   10.050    0.000    0.202    0.683
##    .hcope_gr          0.239    0.024   10.050    0.000    0.239    0.660
##    .amphi_m_gr        0.633    0.063   10.050    0.000    0.633    0.802
##    .rotif_m_gr        1.861    0.185   10.050    0.000    1.861    0.689
##    .pcope_gr          0.577    0.057   10.050    0.000    0.577    0.693
##    .mysid_gr          1.057    0.105   10.050    0.000    1.057    0.713
##    .estfsh_bsmt_gr    1.804    0.180   10.050    0.000    1.804    0.663
## 
## R-Square:
##                    Estimate
##     chla_gr           0.317
##     hcope_gr          0.340
##     amphi_m_gr        0.198
##     rotif_m_gr        0.311
##     pcope_gr          0.307
##     mysid_gr          0.287
##     estfsh_bsmt_gr    0.337
summary(modfitN, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 60 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        83
##                                                       
##                                                   Used       Total
##   Number of observations                           186         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                28.094
##   Degrees of freedom                                22
##   P-value (Chi-square)                           0.173
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla_gr ~                                                              
##     chla_1            -0.405    0.047   -8.687    0.000   -0.405   -0.540
##     hcope_1            0.035    0.059    0.603    0.546    0.035    0.041
##     amphi_m_1          0.027    0.045    0.600    0.548    0.027    0.036
##     rotif_m_1          0.004    0.043    0.087    0.931    0.004    0.005
##     corbic_1          -0.016    0.044   -0.352    0.725   -0.016   -0.022
##     flow              -0.016    0.047   -0.332    0.740   -0.016   -0.023
##     turbid             0.028    0.044    0.630    0.529    0.028    0.041
##     temp               0.044    0.043    1.019    0.308    0.044    0.066
##   hcope_gr ~                                                             
##     chla_1            -0.004    0.062   -0.070    0.944   -0.004   -0.004
##     hcope_1           -0.799    0.101   -7.874    0.000   -0.799   -0.603
##     pcope_1           -0.141    0.063   -2.256    0.024   -0.141   -0.140
##     mysid_1            0.070    0.084    0.825    0.409    0.070    0.064
##     corbic_1           0.156    0.057    2.709    0.007    0.156    0.143
##     flow              -0.388    0.072   -5.380    0.000   -0.388   -0.366
##     turbid             0.122    0.069    1.760    0.078    0.122    0.116
##     temp               0.019    0.065    0.291    0.771    0.019    0.018
##     estfish_bsmt_1    -0.001    0.065   -0.022    0.983   -0.001   -0.001
##   amphi_m_gr ~                                                           
##     chla_1             0.065    0.072    0.894    0.371    0.065    0.058
##     amphi_m_1         -0.503    0.070   -7.203    0.000   -0.503   -0.453
##     flow               0.006    0.073    0.080    0.936    0.006    0.006
##     turbid            -0.060    0.068   -0.888    0.374   -0.060   -0.060
##     temp              -0.034    0.066   -0.513    0.608   -0.034   -0.034
##     estfish_bsmt_1    -0.058    0.067   -0.872    0.383   -0.058   -0.061
##   rotif_m_gr ~                                                           
##     chla_1            -0.188    0.138   -1.357    0.175   -0.188   -0.076
##     rotif_m_1         -1.439    0.128  -11.262    0.000   -1.439   -0.649
##     flow               0.663    0.143    4.627    0.000    0.663    0.294
##     turbid            -0.147    0.133   -1.107    0.268   -0.147   -0.066
##     temp              -0.123    0.129   -0.960    0.337   -0.123   -0.056
##     estfish_bsmt_1    -0.070    0.130   -0.539    0.590   -0.070   -0.033
##   pcope_gr ~                                                             
##     hcope_1           -0.053    0.169   -0.311    0.756   -0.053   -0.023
##     pcope_1           -1.040    0.106   -9.795    0.000   -1.040   -0.603
##     mysid_1            0.196    0.143    1.368    0.171    0.196    0.105
##     corbic_1           0.069    0.102    0.676    0.499    0.069    0.037
##     flow              -0.271    0.121   -2.236    0.025   -0.271   -0.149
##     turbid            -0.293    0.116   -2.527    0.012   -0.293   -0.164
##     temp               0.040    0.109    0.367    0.713    0.040    0.023
##     estfish_bsmt_1    -0.180    0.110   -1.634    0.102   -0.180   -0.107
##     chla_1             0.273    0.114    2.404    0.016    0.273    0.138
##   mysid_gr ~                                                             
##     hcope_1            0.094    0.306    0.307    0.759    0.094    0.023
##     pcope_1           -0.182    0.193   -0.944    0.345   -0.182   -0.057
##     mysid_1           -2.365    0.257   -9.189    0.000   -2.365   -0.692
##     amphi_m_1         -0.309    0.182   -1.696    0.090   -0.309   -0.085
##     flow              -1.146    0.216   -5.309    0.000   -1.146   -0.344
##     turbid             0.807    0.208    3.886    0.000    0.807    0.245
##     temp               0.146    0.191    0.763    0.446    0.146    0.045
##     estfish_bsmt_1     0.098    0.198    0.492    0.623    0.098    0.032
##   estfish_bsmt_gr ~                                                      
##     estfish_bsmt_1    -1.403    0.175   -8.001    0.000   -1.403   -0.572
##     hcope_1            0.191    0.275    0.695    0.487    0.191    0.058
##     pcope_1           -0.185    0.176   -1.055    0.291   -0.185   -0.074
##     amphi_m_1         -0.067    0.183   -0.366    0.714   -0.067   -0.023
##     rotif_m_1          0.083    0.175    0.474    0.635    0.083    0.032
##     mysid_1            0.318    0.230    1.383    0.167    0.318    0.117
##     flow              -0.486    0.194   -2.510    0.012   -0.486   -0.184
##     turbid             0.102    0.184    0.553    0.580    0.102    0.039
##     temp              -0.027    0.169   -0.162    0.871   -0.027   -0.011
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla_gr ~~                                                            
##    .hcope_gr          0.111    0.037    3.013    0.003    0.111    0.227
##    .amphi_m_gr        0.047    0.038    1.249    0.212    0.047    0.092
##    .rotif_m_gr        0.069    0.073    0.943    0.346    0.069    0.069
##    .pcope_gr         -0.118    0.061   -1.935    0.053   -0.118   -0.143
##    .mysid_gr          0.087    0.108    0.804    0.421    0.087    0.059
##    .estfsh_bsmt_gr   -0.112    0.095   -1.182    0.237   -0.112   -0.087
##  .hcope_gr ~~                                                           
##    .amphi_m_gr        0.044    0.056    0.783    0.434    0.044    0.057
##    .rotif_m_gr       -0.326    0.111   -2.925    0.003   -0.326   -0.220
##    .pcope_gr          0.336    0.093    3.606    0.000    0.336    0.274
##    .mysid_gr          1.055    0.178    5.928    0.000    1.055    0.483
##    .estfsh_bsmt_gr   -0.166    0.142   -1.167    0.243   -0.166   -0.086
##  .amphi_m_gr ~~                                                         
##    .rotif_m_gr       -0.175    0.115   -1.531    0.126   -0.175   -0.113
##    .pcope_gr         -0.340    0.097   -3.489    0.000   -0.340   -0.265
##    .mysid_gr          0.156    0.168    0.927    0.354    0.156    0.068
##    .estfsh_bsmt_gr   -0.212    0.149   -1.425    0.154   -0.212   -0.105
##  .rotif_m_gr ~~                                                         
##    .pcope_gr          0.140    0.183    0.766    0.443    0.140    0.056
##    .mysid_gr         -0.824    0.330   -2.496    0.013   -0.824   -0.186
##    .estfsh_bsmt_gr    0.117    0.286    0.410    0.682    0.117    0.030
##  .pcope_gr ~~                                                           
##    .mysid_gr          0.903    0.276    3.265    0.001    0.903    0.247
##    .estfsh_bsmt_gr    0.435    0.239    1.821    0.069    0.435    0.135
##  .mysid_gr ~~                                                           
##    .estfsh_bsmt_gr   -0.069    0.421   -0.165    0.869   -0.069   -0.012
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla_gr           0.330    0.034    9.644    0.000    0.330    0.700
##    .hcope_gr          0.733    0.076    9.644    0.000    0.733    0.652
##    .amphi_m_gr        0.801    0.083    9.644    0.000    0.801    0.773
##    .rotif_m_gr        3.006    0.312    9.644    0.000    3.006    0.593
##    .pcope_gr          2.055    0.213    9.644    0.000    2.055    0.626
##    .mysid_gr          6.521    0.676    9.644    0.000    6.521    0.589
##    .estfsh_bsmt_gr    5.066    0.525    9.644    0.000    5.066    0.729
## 
## R-Square:
##                    Estimate
##     chla_gr           0.300
##     hcope_gr          0.348
##     amphi_m_gr        0.227
##     rotif_m_gr        0.407
##     pcope_gr          0.374
##     mysid_gr          0.411
##     estfsh_bsmt_gr    0.271
summary(modfitS, standardized=T, rsq=T)
## lavaan 0.6-11 ended normally after 41 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        81
##                                                       
##                                                   Used       Total
##   Number of observations                           192         312
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                23.144
##   Degrees of freedom                                24
##   P-value (Chi-square)                           0.511
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   chla_gr ~                                                              
##     chla_1            -0.486    0.050   -9.676    0.000   -0.486   -0.599
##     hcope_1            0.063    0.046    1.362    0.173    0.063    0.079
##     clad_1             0.119    0.044    2.675    0.007    0.119    0.159
##     rotif_m_1         -0.044    0.046   -0.956    0.339   -0.044   -0.055
##     corbic_1          -0.015    0.045   -0.331    0.741   -0.015   -0.019
##     flow              -0.081    0.051   -1.570    0.116   -0.081   -0.098
##     turbid             0.003    0.047    0.069    0.945    0.003    0.004
##     temp               0.018    0.046    0.393    0.694    0.018    0.023
##   hcope_gr ~                                                             
##     chla_1             0.148    0.058    2.540    0.011    0.148    0.158
##     hcope_1           -0.460    0.057   -8.048    0.000   -0.460   -0.495
##     pcope_1            0.008    0.054    0.155    0.877    0.008    0.009
##     corbic_1           0.099    0.055    1.789    0.074    0.099    0.107
##     flow              -0.119    0.062   -1.926    0.054   -0.119   -0.125
##     turbid            -0.086    0.058   -1.493    0.136   -0.086   -0.096
##     temp               0.059    0.057    1.037    0.300    0.059    0.066
##     estfish_bsmt_1    -0.136    0.056   -2.439    0.015   -0.136   -0.152
##   clad_gr ~                                                              
##     chla_1             0.216    0.083    2.600    0.009    0.216    0.174
##     clad_1            -0.561    0.077   -7.252    0.000   -0.561   -0.488
##     pcope_1            0.019    0.076    0.249    0.803    0.019    0.016
##     flow               0.222    0.084    2.656    0.008    0.222    0.176
##     turbid            -0.099    0.079   -1.252    0.211   -0.099   -0.083
##     temp               0.018    0.078    0.229    0.819    0.018    0.015
##     estfish_bsmt_1    -0.107    0.075   -1.420    0.156   -0.107   -0.090
##   amphi_m_gr ~                                                           
##     chla_1            -0.041    0.095   -0.430    0.667   -0.041   -0.025
##     amphi_m_1         -0.885    0.089   -9.913    0.000   -0.885   -0.594
##     flow              -0.075    0.100   -0.750    0.453   -0.075   -0.045
##     turbid             0.198    0.096    2.068    0.039    0.198    0.127
##     temp               0.082    0.093    0.881    0.378    0.082    0.053
##     estfish_bsmt_1     0.061    0.094    0.646    0.519    0.061    0.039
##   rotif_m_gr ~                                                           
##     chla_1            -0.003    0.101   -0.031    0.975   -0.003   -0.002
##     rotif_m_1         -1.058    0.096  -10.988    0.000   -1.058   -0.603
##     flow               0.365    0.105    3.479    0.001    0.365    0.204
##     turbid            -0.089    0.098   -0.911    0.362   -0.089   -0.053
##     temp              -0.036    0.097   -0.372    0.710   -0.036   -0.022
##     estfish_bsmt_1     0.171    0.096    1.780    0.075    0.171    0.103
##   pcope_gr ~                                                             
##     chla_1             0.373    0.100    3.717    0.000    0.373    0.230
##     hcope_1           -0.115    0.098   -1.176    0.240   -0.115   -0.072
##     clad_1             0.112    0.095    1.179    0.238    0.112    0.075
##     pcope_1           -0.764    0.096   -8.001    0.000   -0.764   -0.493
##     corbic_1           0.046    0.093    0.491    0.624    0.046    0.029
##     flow               0.003    0.106    0.026    0.979    0.003    0.002
##     turbid            -0.091    0.098   -0.932    0.351   -0.091   -0.059
##     temp              -0.002    0.095   -0.018    0.986   -0.002   -0.001
##     estfish_bsmt_1    -0.053    0.097   -0.553    0.580   -0.053   -0.035
##   estfish_bsmt_gr ~                                                      
##     estfish_bsmt_1    -1.434    0.174   -8.228    0.000   -1.434   -0.517
##     hcope_1            0.010    0.175    0.059    0.953    0.010    0.004
##     pcope_1           -0.204    0.175   -1.164    0.244   -0.204   -0.073
##     amphi_m_1          0.230    0.158    1.454    0.146    0.230    0.087
##     rotif_m_1         -0.015    0.173   -0.086    0.931   -0.015   -0.005
##     clad_1             0.251    0.167    1.506    0.132    0.251    0.093
##     flow              -0.151    0.191   -0.790    0.430   -0.151   -0.051
##     turbid             0.513    0.178    2.880    0.004    0.513    0.184
##     temp              -0.037    0.172   -0.217    0.828   -0.037   -0.013
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .chla_gr ~~                                                            
##    .hcope_gr          0.126    0.036    3.460    0.001    0.126    0.258
##    .clad_gr           0.198    0.050    3.951    0.000    0.198    0.298
##    .amphi_m_gr        0.016    0.058    0.273    0.785    0.016    0.020
##    .rotif_m_gr        0.184    0.062    2.956    0.003    0.184    0.218
##    .pcope_gr         -0.003    0.059   -0.046    0.963   -0.003   -0.003
##    .estfsh_bsmt_gr   -0.077    0.107   -0.724    0.469   -0.077   -0.052
##  .hcope_gr ~~                                                           
##    .clad_gr           0.182    0.060    3.033    0.002    0.182    0.224
##    .amphi_m_gr        0.104    0.071    1.466    0.143    0.104    0.106
##    .rotif_m_gr       -0.188    0.075   -2.498    0.012   -0.188   -0.183
##    .pcope_gr          0.093    0.072    1.293    0.196    0.093    0.094
##    .estfsh_bsmt_gr   -0.300    0.131   -2.285    0.022   -0.300   -0.167
##  .clad_gr ~~                                                            
##    .amphi_m_gr        0.067    0.097    0.686    0.492    0.067    0.050
##    .rotif_m_gr        0.172    0.102    1.682    0.093    0.172    0.122
##    .pcope_gr          0.071    0.099    0.716    0.474    0.071    0.052
##    .estfsh_bsmt_gr   -0.297    0.179   -1.660    0.097   -0.297   -0.121
##  .amphi_m_gr ~~                                                         
##    .rotif_m_gr       -0.089    0.123   -0.727    0.467   -0.089   -0.053
##    .pcope_gr         -0.155    0.119   -1.298    0.194   -0.155   -0.094
##    .estfsh_bsmt_gr   -0.666    0.220   -3.033    0.002   -0.666   -0.224
##  .rotif_m_gr ~~                                                         
##    .pcope_gr          0.420    0.128    3.276    0.001    0.420    0.243
##    .estfsh_bsmt_gr    0.438    0.227    1.935    0.053    0.438    0.141
##  .pcope_gr ~~                                                           
##    .estfsh_bsmt_gr    0.742    0.224    3.307    0.001    0.742    0.246
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .chla_gr           0.399    0.041    9.798    0.000    0.399    0.662
##    .hcope_gr          0.593    0.061    9.798    0.000    0.593    0.736
##    .clad_gr           1.113    0.114    9.798    0.000    1.113    0.783
##    .amphi_m_gr        1.621    0.165    9.798    0.000    1.621    0.658
##    .rotif_m_gr        1.775    0.181    9.798    0.000    1.775    0.625
##    .pcope_gr          1.675    0.171    9.798    0.000    1.675    0.694
##    .estfsh_bsmt_gr    5.441    0.555    9.798    0.000    5.441    0.694
## 
## R-Square:
##                    Estimate
##     chla_gr           0.338
##     hcope_gr          0.264
##     clad_gr           0.217
##     amphi_m_gr        0.342
##     rotif_m_gr        0.375
##     pcope_gr          0.306
##     estfsh_bsmt_gr    0.306
#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
zoop_plot_far_west <- createGraph(fit=modfitFW, 
                                  reference_df=cnameslag, 
                                  model_type="monthly_zoop",
                                  region="Far West",
                                  title="Far West",
                                  manual_port_settings=TRUE)
zoop_plot_far_west
#WEST
zoop_plot_west <- createGraph(fit=modfitW, 
                              reference_df=cnameslag, 
                              model_type="monthly_zoop",
                              region="West",
                              title="West",
                              manual_port_settings=TRUE)
zoop_plot_west
#NORTH
zoop_plot_north <- createGraph(fit=modfitN, 
                               reference_df=cnameslag, 
                               model_type="monthly_zoop",
                               region="North",
                               title="North",
                               manual_port_settings=TRUE)
zoop_plot_north
#SOUTH
zoop_plot_south <- createGraph(fit=modfitS, 
                               reference_df=cnameslag, 
                               model_type="monthly_zoop",
                               region="South",
                               title="South",
                               manual_port_settings=TRUE)
zoop_plot_south